Journal of Artificial Intelligence and Engineering Applications (JAIEA)

Design and Construction of a Website-Based Posyandu Service System in Sukaraja Village

2025-08-05T20:42:32+07:00

Posyandu services at the auxiliary health center are a place to carry out health service activities. The auxiliary health center (pustu) still carries out posyandu service activities for the community. Posyandu services are located in Sukaraja Village, South Prabumulih. Currently, health services in Sukaraja Village are still providing services manually and do not have a website. The purpose of this study is to build a website so that officers and the community can more easily record identity data. Based on the results of the study, the researcher tried to build a web-based service system using a qualitative descriptive method by collecting data in the form of observations, interviews, literature studies and developed using the Rapid Application Development (RAD) method. System design tools use Use Case, Activity Diagram and Class Diagram.

Website Design for the Sukarami Village Head's Office

2025-08-05T20:15:31+07:00

The Sukarami village head office is under the auspice of the village government which has an Organization structure the sukarami village head office is the village head office located in sukarami village,rambang district, muara enim regency, south Sumatra province.Currently, the sukarami village head office does not have a website so that everyone can find out about the village head office to find out information such as village profiles, history of village activities, village programs,village structures, infrastructure owned by the village, and information on social assistance from the government and regions.The purpose of this study is to build a website so that the community can more easily find out about what is in the sukarami village head office. The research method uses a descriptive qualitative method with data collection in the form of observation,interviews and literature studies.The type of data consists of qualitative data, and data sources consist of primary data and secondary data.The device development method uses the rapid application development method.The system design tools used are the care diagram class diagram the website uses the PHP and MySQL programming languages.

Implementation of the Rapid Application Development (RAD) Model in the Web-Based MSME Product Sales System

2025-08-05T20:52:01+07:00

A web-based sales system is an effective solution to help micro, small, and medium enterprises (UMKM) manage sales efficiently and in a structured manner. This research was conducted at Anisa Store, one of the UMKM in Majasari Village, Prabumulih, which still uses a manual sales process, so it has difficulty in recording data. The purpose of this study is to build a web -based sales system using the Rapid Application Development (RAD) Model, which allows the system development process to be carried out quickly and actively involves users. The RAD model consists of several stages, namely needs planning, design, construction and relocation. The result of this study is a web-based sales system that can be used by the owner of Anisa Store to manage product data, sales transactions, and shipping. By implementing this system, it is expected to increase the marketing reach of Anisa Store.

Implementation of Village Management Information System for Monitoring and Evaluation of Pangkul Village Development Program

2025-08-05T20:56:09+07:00

Pangkul Village is optimizing monitoring and evaluation of development programs through the implementation of a new information system. This system was developed using agile methods to adapt to rapidly changing needs. By implementing agile methods in monitoring and evaluating village development programs, it is hoped that a more adaptive and results-oriented process can be created that is beneficial to the community. This activity aims to identify progress, challenges, and impacts of development programs, as well as ensure community participation in the village development process. The evaluation results show the development of village government performance in planning and implementation, as well as the identification of relevant performance indicators for continuous improvement

Android-Based School Exam Information System for Sixth Grade Students at SD Negeri 23 Prabumulih

2025-08-05T21:11:17+07:00

The school examination information system is a digital platform that aids in the administration of examinations for 6th-grade elementary school students. This research aims to develop an efficient and effective school examination information system to enhance the examination administration process at the elementary school level. The system development method waterfall and utilizes an object-oriented software design approach, considering the specific needs of elementary school examination administration. The developed system encompasses various features, such as examination scheduling, student data management, examination result recording, and provision of evaluation reports. The implementation of this system is expected to provide convenience for schools in managing and conducting school examinations, thereby facilitating smoother and more efficient learning processes.

Design and Development of a Web-Based Integrated Health Post Application for Infants, Toddlers, and Pregnant Women at the Belida Darat Community Health Center

2025-08-07T08:07:30+07:00

Belida Darat Health Center is under the auspices of the Muara Enim Regency Health Office, this Health Center is located in Belida Darat District, precisely in Tanjung Bunut Village. Currently, Belida Darat Health Center does not have an application for managing data on infants, toddlers and pregnant women, so the purpose of this study is to build a posyandu application to facilitate the management of data on infants, toddlers and pregnant women at Belida Darat Health Center. The research method uses a qualitative descriptive method with data collection techniques in the form of observation, interviews and literature studies, data sources consist of primary data and secondary data. while for the system development method using the RAD (Rapid Application Development) method, the system design tool used is UML (unified modeling Language). The design of this application uses the PHP (Processor Hypertext) programming language, MySQL Database and Coding using Visual Studio Code.

Website-Based Administrative Governance Information System in Lubuk Semantung Village

2025-08-07T08:08:18+07:00

Lubuk Semantung Village is optimizing administrative governance through the implementation of a new information system. This system was developed using agile methods to adapt to rapidly changing needs. With the integration of modules such as population data management, financial management, activity documentation, and reporting, this system aims to improve efficiency and transparency. Initial results from the implementation show a significant increase in the accessibility of information for residents and the accountability of the village government. This information system is expected to be a pilot model for other villages in improving the quality of administrative governance.

Design and Development of a Desktop-Based Cashier Application at the Legowo Satay Restaurant

2025-08-06T23:21:36+07:00

The advancement of information technology encourages various business sectors to adapt, including in managing sales transactions. Resto Sate Legowo, as a local culinary business, still relies on manual recording for cashier operations, which is prone to errors and takes considerable time. To address this issue, this study aims to design and develop a desktop-based cashier application to help accelerate and simplify the transaction process at Resto Sate Legowo. The development method used in this system is Rapid Application Development (RAD), with Unified Modeling Language (UML) as the design tool. This method allows for faster development by actively involving users in each stage of the process. The application is built using the Java programming language and MySQL as the database to store transaction and menu data. The result of this study is a cashier application capable of recording sales transactions, printing receipts, and generating daily reports automatically. System testing shows that the application functions properly and provides convenience for staff in managing transactions. With this application, it is expected that operational efficiency at Resto Sate Legowo can be significantly improved.

Implementation of the RAD Method in the Design and Development of the Melati Library Information System Tebat Agung Village Web-Based

2025-08-10T07:05:19+07:00

The development of information technology offers solutions for library management, including at Melati Library in Tebat Agung Village, Muara Enim Regency, South Sumatra, which still uses a manual system. Issues include recording, book borrowing and returning, searching, and report generation, all of which are inefficient. This study aims to design a web-based library information system using the Rapid Application Development (RAD) method, which enables fast and structured system development. The system was developed using a descriptive qualitative approach, UML modeling, PHP programming language, and MySQL database. The resulting system simplifies book data management and is expected to improve the efficiency and quality of library services overall.

Web-Based Design and Construction of HMI Member Registration for Prabumulih City

2025-08-10T21:18:18+07:00

The Islamic Students Association (HMI) Prabumulih Branch still carries out its member registration process conventionally, such as using physical forms and manual data collection, which can lead to delays and disorganized data management. This study aims to design and develop a web-based member registration system that simplifies administrative processes and improves committee work efficiency. The system development methodology used in this study is the Waterfall method, which includes requirement analysis, system design, implementation, testing, and maintenance. The result of this research is a web-based registration system that can be accessed online by prospective members and the organizing committee, featuring digital form filling, document uploads, and member data management. This system is expected to support a more structured, efficient, and modern registration process within the HMI Prabumulih Branch organization.

Design of a Web-Based Digital Archive Information System for Incoming and Outgoing Mail Case Study of the Rambang Kapak Tengah District Office

2025-08-14T09:49:35+07:00

Well-organized archives are an important element in supporting smooth operations and public services in government agencies. The Rambang Kapak Tengah Subdistrict Office still uses manual methods in managing records. This is often inefficient and susceptible to damage and data loss. This research aims to design a web-based digital archive information system that is able to increase efficiency and reliability in managing archives in the office. The method used is the RAD development model which includes requirements analysis, design, implementation and testing stages. The resulting system makes it easy to store, search and manage archives digitally. This helps minimize the risk of data loss and improves ease of access and security of information. It is hoped that the implementation of this system can support more effective and efficient archive management at the Tanjung Rambang village sub-district office.

Development of a Website-Based Cashier Application at the NBO Prabumulih Store Using the RAD Method

2025-08-21T16:47:54+07:00

NBO Prabumulih Store is a store that sells various kinds of men's and women's clothing, NBO Prabumulih Store is located in North Prabumulih District, precisely in Anak Petai Village. Currently, NBO Prabumulih Store does not have a cashier application for managing payment transactions, so the purpose of this study is to build a cashier application to facilitate payment transactions at NBO Prabumulih Store. This research method uses a qualitative descriptive method with data collection techniques in the form of observation, interviews and literature studies, data sources consist of primary data and secondary data, while for the system development method using the RAD (Rapid Application Development) method, the system design tool used is UML (Unified Modeling Language). The design of this application uses the PHP (Processor Hypertext) programming language, MySQL Database and Coding using Visual Studio Code

Analysis of Hate Speech Againts Gojek Drivers using the Naïve Bayes Algorithm on the Facebook Platform

2026-04-16T15:54:45+07:00

Social media has become a space where many individuals express their opinions freely, including negative comments that may lead to hate speech. One group often targeted by such speech is Gojek drivers. This study aims to classify user comments on Facebook into two sentiment categories: positive and negative, with a primary focus on negative comments. The data was collected from public Facebook posts using the APIFY scraping tool. After the data was gathered, several preprocessing stages were carried out, including case folding, cleaning, tokenization, normalization, stopword removal, and stemming. The text data was then converted into numerical form using CountVectorizer. The classification algorithm used in this research is Naive Bayes with the MultinomialNB model, as the input data consists of word frequency. The results of the model evaluation show that this algorithm performs well in classifying negative comments, especially in identifying word patterns that commonly appear in hate speech directed toward Gojek drivers.

Sentiment Analysis of Pre-Loved Shoe Product Sales Based on X Reviews with a Comparison of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Algorithms

2026-04-16T15:59:12+07:00

The rapid growth of social media enables consumers to express opinions about products openly, including preloved shoes. These reviews are crucial as they can influence purchase intentions and brand perception. This study aims to analyze user reviews on the X (Twitter) platform using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms. A total of 1,005 reviews were collected, then preprocessed and balanced into 738 data consisting of positive and negative sentiments. The results show that SVM achieved an accuracy of 68%, while LSTM obtained 61.49% in its best configuration. Thus, SVM demonstrates better efficiency in classifying simple text, whereas LSTM requires more complex parameters to achieve optimal performance. This research is expected to serve as a reference for utilizing sentiment analysis to support business decision-making in the preloved product market.

Web-Based Goods Inventory Information System Using the Rapid Application Development Method (Case Study: SMK Fatahillah Cileungsi Bogor)

2026-01-18T19:04:26+07:00

Inventory management at SMK Fatahillah Cileungsi Bogor is not yet supported by an integrated information system, so the process of recording incoming and outgoing goods has not been running optimally. This condition causes difficulties in data management, increases the risk of inventory information discrepancies, and limitations in monitoring real-time availability of goods. This study aims to design and implement a web-based inventory information system that is able to automate and centralize school asset management. The system development method used is Rapid Application Development (RAD), which includes the stages of requirements planning, system design, prototype construction, testing, and implementation. The developed system provides features for managing master data on goods, user management, recording incoming and outgoing goods transactions, borrowing and returning goods, and automatic generation of inventory reports. Test results show that the system can function well according to user needs. The implementation of this web-based information system has been proven to be able to improve data accuracy, accelerate the information search process, and optimize the effectiveness of asset management at SMK Fatahillah Cileungsi Bogor.

Web-Based Cooperative Management Information System Using Agile Method (Case Study: Bungah Bareng Mandiri Banyumas)

2026-01-18T19:03:58+07:00

The development of information technology encourages cooperatives to manage operational activities in an integrated manner to improve the effectiveness and accuracy of data management. The Bungah Bareng Mandiri Banyumas Cooperative still faces obstacles in managing cash and credit transactions, recording customer data, installment payments, and preparing reports because the system used is not yet fully integrated. This study aims to design and implement a website-based cooperative management information system that is able to integrate all operational data into a centralized platform. The research method used is applied research with a qualitative-descriptive approach, while system development is carried out using the Agile method to accommodate user needs iteratively. The implementation results confirm that the improved system is able to increase the regularity and accuracy of data processing, facilitate monitoring of installment due dates, and support the preparation of operational reports more quickly and structured. Thus, the implementation of this information system contributes to increasing the effectiveness, efficiency, and transparency of the operational management of the Bungah Bareng Mandiri Banyumas Cooperative.

Classification of Herbal Leaves Using Support Vector Machine (SVM)

2026-03-03T07:54:18+07:00

Indonesia is a country with high biodiversity, including various types of herbal leaves with potential use as traditional medicine. Manual identification of herbal leaves often encounters challenges due to morphological similarities among species and the limited availability of experts, thereby necessitating a fast and accurate technology-based classification method. This study aims to classify 10 types of herbal leaves using the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel. The dataset consists of 3,500 leaf images (350 images per class), from which color features (HSV), texture features (Gray Level Co-occurrence Matrix/GLCM), and shape features (area, perimeter, and aspect ratio) were extracted. The research process includes preprocessing, feature extraction, data splitting into training and testing sets, model training, and performance evaluation. Evaluation was conducted using a confusion matrix, with accuracy as the primary metric due to the balanced class distribution. Precision, recall, and F1-score were employed as supporting evaluation metrics. The results indicate that the SVM model with an RBF kernel successfully classified the 10 types of herbal leaves with an accuracy of 81.29%. Based on per-class analysis, the highest performance was achieved in the Papaya class with an F1-score of 90.00%, followed by Jambu Biji (89.36%) and Pandan (87.14%). In contrast, the lowest performance was observed in the Aloe Vera class with an F1-score of 65.71% and Lime with 70.00%. The model achieved an average precision of 81.16%, recall of 80.73%, and F1-score of 80.94%. Misclassifications primarily occurred among classes with high morphological similarity, such as Aloe Vera, which was frequently misclassified as Pandan (9 cases) and Basil (5 cases). The system has been implemented as a Graphical User Interface (GUI) application that allows users to upload leaf images and obtain classification results along with information regarding their herbal benefits within 1–2 seconds.

Implementation of Agile Method in Village Web Development (Case Study: Tapus Village)

2025-08-08T11:50:56+07:00

The advancement of information technology has had a significant impact on various aspects of life, including governance at the village level. However, Tapus Village still relies on traditional methods such as bulletin boards, community meetings, and WhatsApp groups to disseminate information to its residents. These methods have limitations in terms of reach, effectiveness, and information archiving. To address these issues, this study aims to develop a village website for Tapus Village using the Agile Software Development methodology. Agile was chosen due to its flexible and adaptive approach, allowing the system to evolve in response to users’ changing needs. The outcome of this research is a village website capable of presenting activity updates and important announcements in a structured and easily accessible format. With the implementation of this website, Tapus Village is expected to enhance transparency, improve service efficiency, and increase community engagement in village development. Moreover, this study demonstrates that the Agile method is effective for developing web-based information systems in village government environments with limited resources.

Implementation of the RAD Method in the Development of a Web-Based Jiwa Baru Village Profile Information System

2025-08-09T18:38:58+07:00

The use of information technology in various aspects of life including government is increasingly important, one of which can feel the positive impact of the application of technology is the village government. In Jiwa Baru Village, the delivery of information is still delivered through speakers or bulletin boards. The limitations in delivering this information often make it difficult for people to get accurate and up-to-date information. The purpose of this study is to design a web-based Jiwa Baru Village Profile Information System which aims to deliver information to the public effectively and efficiently. The method used in this study uses the RAD (Rapid Application Development) method. The results of the evaluation show that all functions of this system run well and in accordance with user needs, with this system it can make it easier for people to find information about the village.

Development of an Ordering Application for Ayek Jerangan Shop in Tanjung Bunut Village Using the Waterfall Method

2025-08-10T21:17:18+07:00

The development of information technology has improved efficiency in various sectors, including clean water distribution. In Tanjung Bunut Village, ayek jerangan (boiled or refillable drinking water) is a primary water source, but its ordering and distribution system remains conventional, causing irregular supply and fake orders. This study aims to design a web-based ordering application to improve efficiency, transparency, and accessibility for residents. The application is expected to help manage demand and distribution in a more structured manner and enhance service quality for the community.

Website-Based Design of Academic Information System at Rama Chindo PAUD

2025-08-13T07:41:59+07:00

In line with the rapid development of technology and information, the demand for quick, accurate, and efficient information systems is growing, especially in the field of education. PAUD Rama Cindo in Prabumulih currently relies on conventional methods for managing academic data, such as grades, attendance, and student progress reports, which are recorded manually in large books and stored in physical files. This situation leads to various challenges, such as difficulties in accessing data, the potential for document loss, and inefficient information dissemination. This study aims to design and develop a website-based academic information system for PAUD Rama Cindo to enhance efficiency in managing academic data and delivering information to teachers, students, and parents. The developed system will enable digital data management, making academic information easily accessible, quick, and accurate. With this system, it is expected that academic data management can be done in an integrated and more efficient manner, while also introducing PAUD Rama Cindo to a wider community through digital media. The research methods include data collection, system design, and testing of the developed academic information system application. This study provides practical contributions to PAUD Rama Cindo in improving the quality of academic services through the application of information technology, while also offering an academic reference for the development of similar information systems in early childhood education.

Web-Based Futsal Field Reservation Application at Futsal NR Prabumulih

2025-08-14T22:51:15+07:00

The development of digital technology encourages the sports sector to improve services. Futsal NR Prabumulih still uses a manual booking system that is less practical, especially in terms of time and access. This study aims to design a web-based futsal field booking application to make it easier for users to view schedules, book futsal fields, and make payments online. With a user-friendly interface, this application allows customers to make reservations at any time. In designing this system, UML (Unified Modeling Language) modeling tools such as use case diagrams, activity diagrams, and class diagrams are used to explain system functions, activity flows, and data structures. The system development method used is Rapid Application Development (RAD), which emphasizes the speed of development through iterative prototyping and active user involvement in every stage of development. Testing of the system shows an increase in efficiency in booking management and an increase in customer satisfaction. This application is expected to support modern and integrated services at Futsal NR Prabumulih.

Implementation of the Rapid Application Development Approach for the Academic Information System at Muzakkir Islamic Primary School Prabumulih

2025-08-16T07:24:05+07:00

This study stems from the limited application of information technology, which continues to influence various aspects of human life—particularly in the fields of employment, business, and, more specifically, at Muzakkir Islamic Elementary School in Prabumulih, where data management is still handled through conventional methods. Data for this research was collected using a descriptive method with a qualitative approach, employing observation, interviews, and literature review. The design of the academic information system applied the Rapid Application Development (RAD) methodology to assist developers in analyzing and designing the system based on actual needs. The Unified Modeling Language (UML) served as a tool for designing the school’s academic information system, supporting the developers during the process. The developed system was implemented using the Hypertext Preprocessor (PHP) programming language and a MySQL database.

Development of Web System for Sales Optimization at CV. CS Swalayan using Association Rule Method

2025-10-24T18:23:27+07:00

CV. CS Swalayan encounters challenges related to declining consumer purchasing power and the underutilization of transactional data for analyzing customer purchasing patterns. This study aims to develop a web-based system employing Association Rule methodology with the Apriori algorithm to optimize sales performance, identify top-selling products, and determine frequently co-purchased product combinations. The research methodology encompasses the collection of 296 sales transaction records for basic commodity products from CV—CS Swalayan during January 2025, followed by data preprocessing procedures. The Apriori algorithm is implemented with minimum support and confidence thresholds set at 0.01 and 0.3, respectively. The web-based system is developed using Python with the Flask framework for backend functionality, MySQL for database management, and validated through black-box testing methodology. The findings reveal the generation of 14 valid and robust association rules, notably "if Selai Srikaya Ngetop is purchased, then Roti Tawar Kupas Ngetop will be purchased" (confidence: 100%; lift ratio: 49.3) and "if Beras Sukaraya Cap Gurih 10KG is purchased, then Minyak Kita Minyak Goreng Sawit 1ltr will be purchased" (confidence: 100%; lift ratio: 16.4). The developed web system successfully passed black-box testing with a 100% success rate. This research contributes by providing a system that enables CV. CS Swalayan will make data-driven decisions to optimize sales strategies, marketing approaches, and inventory management practices.

Web-Based Operational Management Information System for Prospective Indonesian Migrant Employees Using Agile Method (Case Study: PT. Bahana Mega Prestasi Bekasi)

2026-01-27T08:39:31+07:00

Bahana Mega Prestasi, an Indonesian Migrant Worker Placement Company (IMWPC), faces challenges in managing operational data for Prospective Indonesian Migrant Workers (PIMW). This is due to the lack of integration of registration, attendance, and eligibility assessment processes within a single information system. This situation increases the administrative burden and the potential for data inconsistencies. This research aims to design and implement a web-based PIMW Operational Management Information System capable of centrally integrating all administrative processes. The system was developed using Agile methods with an iterative approach to ensure the system meets user needs. The system was built using the PHP programming language with the Laravel framework and a MySQL database. Implementation results indicate that the developed system is able to support the PIMW registration process, attendance monitoring, and candidate eligibility evaluation in a more structured and real-time manner. The implementation of this system contributes to improving the orderliness of data management, supporting managerial decision-making, and increasing operational efficiency at PT. Bahana Mega Prestasi.

Comparison of Naive Bayes and KNN Algorithms for Heart Attack Disease Classification

2026-02-27T14:42:34+07:00

This Heart attack is one of the leading causes of death worldwide and requires early diagnosis to reduce fatal risks. This study aims to compare the performance of the Naive Bayes and K-Nearest Neighbors (KNN) algorithms in classifying heart attack disease. The dataset used consists of medical records containing clinical parameters such as age, blood pressure, cholesterol level, and heart rate. The research methodology includes data preprocessing, splitting the dataset into training and testing sets, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The results show that Naive Bayes demonstrates advantages in computational speed and performs well on smaller datasets, achieving an accuracy of 85%. In contrast, KNN provides better performance on larger datasets, reaching an accuracy of 90%, particularly when the optimal K value is applied. These findings indicate that algorithm selection for heart attack classification depends on dataset characteristics and specific implementation needs. This study is expected to contribute to the development of artificial intelligence–based clinical decision support systems for early heart attack diagnosis and improved healthcare outcomes.

Implementation of Deep Learning Based on Convolutional Neural Network for Detecting Images of Solar Panel Damage in Smart Grid Systems

2026-03-08T21:46:25+07:00

This study aims to implement Deep Learning based on Convolutional Neural Network (CNN) in detecting solar panel damage using thermal images as part of a Smart Grid system. The main problem addressed is the difficulty of early automatic identification of solar panel cell damage using conventional methods. Through the CNN approach, this study developed a classification model to distinguish between damaged (Defective) and undamaged (Non-Defective) solar panel conditions. The research stages included thermal image dataset collection, pre-processing, model training, and performance evaluation. The results showed that the CNN model was able to achieve an accuracy of over 87% with stable performance on the validation data. Visualization using the Grad-CAM method helps interpret the damaged areas that are the focus of the model's decision.

Sentiment Analysis of Social Media X Users Toward Finance Minister Purbaya Yudhi Sadewa Using the Support Vector Machine Algorithm

2026-03-10T17:57:32+07:00

In this digital era, the rapid advancement of information and communication technology has transformed social media platforms particularly X (formerly Twitter) into a primary space for public discourse concerning government policies. The Minister of Finance, Purbaya Yudhi Sadewa, has become a focal point of public debate, garnering reactions ranging from appreciation to criticism regarding his management of national finances. However, manual sentiment analysis is impractical, time-consuming, and prone to subjectivity when handling the massive and continuously expanding volume of social media data. Therefore, an automated, machine learning-based approach is essential to process this big data into strategic insights for mapping public sentiment. This study aims to objectively analyze public sentiment toward the Minister of Finance by implementing the Support Vector Machine (SVM) algorithm within the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework. The methodology includes data crawling, text preprocessing, and feature extraction using the TF-IDF (Term Frequency – Inverse Document Frequency) method. Analysis of 3,927 tweets reveals that public opinion is dominated by negative sentiment at 54.2%, followed by positive sentiment at 36.9% and neutral sentiment at 8.9%. The developed SVM model achieved a classification accuracy of 72.43%, demonstrating that this machine learning approach is both effective and reliable for mapping public perception. These findings indicate that the Minister of Finance, Purbaya Yudhi Sadewa, faces significant public scrutiny, and this data-driven analysis serves as a strategic tool for evaluating the policies under his administration.

Failure Analysis of Switching Scheme Failures in Loop Protect Multiplexer Telecommunication Networks at PT. PLN (Persero) UP2B DKI Jakarta & Banten

2026-03-13T13:11:45+07:00

PLN (Persero), through UIP2B JAMALI, relies on a loop-topology Loop Protect Multiplexer as its telecommunications backbone to support real-time SCADA, VoIP, and protection services. However, from 2022 to 2024, 36 switching failure incidents occurred in UP2B DKI Jakarta–Banten. This study analyzes the root causes, operational impacts, and recommendations for continuous system reliability improvement. The research employs a case study method and the PPDIOO approach to examine switching failures of the Loop Protect Multiplexer in UP2B DKI Jakarta–Banten. Data were collected through observations of fault history (2022–2024) and interviews. BER testing and QoS parameters refer to the ITU-T Y.1564 standard to formulate recommendations for improving the reliability of the 150 kV backbone network. Testing results indicate that under normal conditions, the system meets SLA requirements in accordance with ITU-T Y.1564, with stable throughput and zero frame loss. However, when one link fails, frame loss occurs during switching despite stable throughput, resulting in SLA failure. The root cause lies in a reactive and non-seamless switching mechanism, creating cross-layer impacts on critical services within PT. PLN (Persero).

Application of the K-Means Clustering Algorithm in the Analysis of Popularity and Growth Trends of Python Packages on the PyPI Dataset

2026-03-16T20:31:32+07:00

The rapid growth of the Python ecosystem has led to an increasing number of packages on the Python Package Index (PyPI), generating a massive volume of download data. This data can be utilized to analyze popularity levels and growth trends of libraries used by the developer community. This study aims to identify popularity patterns and growth trends of Python packages using the K-Means Clustering algorithm. The dataset was obtained from PyPI via the Google BigQuery platform with a one-year observation period using a 1% sampling technique. The pre-processing stage included a filtering process to select the 100 packages with the highest number of downloads and the formation of six main features representing the characteristics of library usage patterns. The data was then normalized using Standard Scaling, while the optimal number of clusters was determined using the Elbow Method and evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. The results showed that the optimal number of clusters is four, with a DBI value of 0.5534 and a Silhouette Score of 0.5748 (the highest among k = 2-10 ), representing the categories of ecosystem foundation libraries, medium-popularity libraries, libraries with concentrated download spikes, and libraries with very rapid usage growth. These results indicate that K-Means Clustering is effective for identifying popularity patterns and library growth trends in large-scale PyPI datasets.

Application of K-Means Clustering: Bot Activity and Sybill Attack Detection on the Solana Blockchain

2026-03-23T11:19:09+07:00

With the development of Blockchain technology, for example, the Solana Blockchain has generated enormous amounts of data and possesses the 5Vs of Big Data: volume, velocity, value, veracity, and variety. This has brought challenges, for example, in distinguishing transactions carried out by humans from automated bots that often carry out market manipulation or Sybil attacks. Therefore, this research aims to detect bot activity on the Solana network by applying data mining techniques, namely the K-Means Clustering algorithm. From the large transaction data that will be extracted only a portion from the public Solana dataset in BigQuery, it will then be processed through a preprocessing stage to normalize the data and simplify complex data into simpler variables before being grouped. Because the extracted data is in the form of unlabeled data groups (unsupervised data), the Clustering Method is used because of its ability to recognize data groups based on behavioral or characteristic similarities without requiring initial data labels (unsupervised learning). The main variables used for the grouping process include transaction frequency, inter-arrival time (inter-transaction), and the number of unique program interactions. The results of this analysis are expected to map transaction accounts into several clusters based on their transaction patterns, allowing for the classification of bots and humans. This research is expected to demonstrate that Big Data infrastructure such as Google Cloud, using data mining techniques (Clustering), can be used to maintain the security and integrity of the blockchain ecosystem.

Implementation of the Heuristic Evaluation Method in the Design of the School Academic Information System Website

2026-03-24T09:45:21+07:00

In the world of education, the role of teachers and parents is very influential in the process of improving student learning achievement. However, in reality, most parents only give responsibility to teachers at school to improve student learning achievement. Parents of students rarely monitor the development of their children's learning abilities due to the lack of information about it. Global Prima National Plus School is one of the leading private schools in Medan, located on Jalan Brigjend Katamso. Currently, Global Prima National Plus School uses Microsoft Excel to manage student data and student test scores. However, the implementation of this system still has several weaknesses, namely parents cannot monitor attendance and directly know the development of student scores and behavior. This will reduce parental participation in their children's educational development. To solve the problems faced by Global Prima National Plus School, an application can be created to monitor student learning development. By using this application, parents of students can obtain information about student attendance data, attitude and behavior scores, assignment scores, and test scores directly, without having to wait for report cards to be distributed. With this web-based student learning progress monitoring application, parents can find out information about student attendance, attitude scores, behavior, exam scores and assignment scores which can be accessed directly through the school website.

Application of K-Means Clustering for Urban Transportation Pattern Analysis Using Big Data Trip Dataset

2026-03-30T13:18:15+07:00

The rapid growth of urban transportation systems has led to the generation of massive amounts of data, commonly referred to as big data. This study aims to analyze transportation patterns using large-scale data obtained from the NYC Taxi Trip Records. The dataset exhibits key big data characteristics, including volume, velocity, and variety. This research applies the K-Means clustering algorithm to group taxi trip data based on features such as trip distance, fare amount, and trip duration. Several preprocessing techniques are performed, including data cleaning, feature engineering, sampling, and normalization. The optimal number of clusters is determined using the Elbow Method and Silhouette Score. The results show that the dataset can be effectively grouped into three clusters representing distinct transportation patterns. These findings demonstrate the capability of clustering techniques in extracting meaningful insights from large-scale datasets and highlight their potential application in urban transportation planning.

Development of a Web-Based System for Recording and Reporting Palm Weights Using Laravel at PT. Graha Prima Lestari

2026-03-30T21:35:07+07:00

This research was initiated by operational problems in the palm oil weighing process, which was conducted manually. The manual method often caused calculation errors, delays in making report , and risk of data loss. To address these issues, a web-based palm oil weighing application was developed using the Laravel framework and the Waterfall development method, supported by a relational database to manage data in an integrated manner.

The application implements a role-based access system to manage permissions for administrators, weighing operators, and management. The system records gross weight, tare weight, and automatically calculates net weight while generating accurate reports efficiently. With this system, the weighing process is expected to become more efficient , precise and structured.

Designing a Web-Based Financial Information System at GKS Palindi using the Rapid Application Development Method

2026-04-02T07:23:22+07:00

In the rapidly evolving information age, technology plays an important role in improving the efficiency of data management, including in church institutions. The Church, as a religious institution with an important role in the spiritual and social life of the people, often faces challenges in financial management, especially in recording congregation donations, operational expenses, and making transparent and accurate financial statements. Good financial management is needed to ensure accountability and transparency, as well as facilitate reporting to the congregation and other related parties. GKS Palindi, a church in Kawangu District with 335 congregations, faces problems in the financial recording system that is still manual using books. This causes data corruption, which risks disrupting the smooth flow of the financial management process. Therefore, this church needs the implementation of a technology-based information system that can facilitate the recording and management of financial transactions efficiently, especially for the six main posts: tithe, thanksgiving, part (household worship), monthly dependents, offerings, and miscellaneous posts. With this system, the church can reduce the potential for human error, monitor cash flow more easily, and provide more accurate and timely financial reports. The right information system can help GKS Palindi in maintaining the continuity of church operations and increasing the congregation's trust in the transparency of financial management. The system development method used is Rapid Application Development (RAD), which allows the creation of a system quickly and responsively to user needs. The implementation of a technology-based recording system is expected to overcome existing problems, as well as support the smooth running of church activities in the long term.

Clusterization of Family Planning Participants Based on Pregnancy Risk Using K-Means Algorithm in Ciherang Village

2026-04-06T16:21:03+07:00

This study aims to group family planning (KB) participants in Ciherang Village based on pregnancy risk levels using the K-Means clustering algorithm. The identification of pregnancy risk is still performed manually, resulting in less effective analysis. Therefore, a data mining approach is applied to improve decision-making accuracy.

The data used in this study were obtained from KB cadres, including variables such as age, number of children, education, occupation, and contraceptive methods. The research method follows the Knowledge Discovery in Database (KDD) stages: data selection, preprocessing, transformation, data mining, and evaluation. The K-Means algorithm is used for clustering, while the Davies–Bouldin Index (DBI) is applied to evaluate clustering quality.

The results show that the optimal number of clusters is K = 2 with a DBI value of 0.721. The first cluster represents low pregnancy risk participants, while the second cluster represents high pregnancy risk participants. Age and number of children are identified as the most influential factors.

This study provides useful insights for healthcare providers in developing targeted strategies for family planning programs.

Keywords: Data Mining; Davies–Bouldin Index; K-Means Clustering; Pregnancy Risk; Family Planning

Prediction of Peritonitis Infection Risk in CAPD Patients using Random Forest Algorithm

2026-04-06T19:55:05+07:00

Peritonitis is a serious complication frequently experienced by patients undergoing Continuous Ambulatory Peritoneal Dialysis (CAPD) and may worsen patient outcomes if not detected early. This study aims to develop a machine learning model to predict peritonitis risk using the Random Forest algorithm and to interpret prediction results using Explainable Artificial Intelligence (XAI). The study utilized a secondary dataset obtained from Kaggle consisting of 20,538 clinical records that were transformed to represent CAPD-related clinical parameters. The research stages included data preprocessing, feature selection using SelectKBest (f_classif), dataset splitting into training and testing sets, model development using Random Forest, and performance evaluation using accuracy, precision, recall, F1-score, and Area Under Curve (AUC). Model interpretability was analyzed using SHAP to identify feature contributions. The experimental results demonstrate that the proposed model achieved an accuracy of 98.70%, precision of 98.22%, recall of 99.24%, F1-score of 98.73%, and AUC of 1.00. The findings indicate that Random Forest provides highly reliable predictive performance and interpretable insights into clinical features influencing peritonitis risk. The developed model has potential to support clinical decision-making systems for early detection of peritonitis risk in CAPD patients.

UI/UX Design of Laundry Pick-Up and Delivery Application using Prototyping Method

2026-04-09T09:58:55+07:00

Digital transformation demands operational efficiency in the conventional laundry industry, which is still hampered by manual management and limited geographic reach.In response to this phenomenon, this research focuses on developing a UI/UX design for the Laundry Express mobile application with superior pickup & delivery service features. The main goal is to reduce the potential for data input errors while providing information transparency for users. Through an iterative prototyping method, the design process includes needs identification and continuous evaluation using Figma. The final product, a high-fidelity prototype, integrates order tracking features, automatic cost calculation based on weight, and a service assessment module. Evaluation using Likert scale for usability measurement demonstrated a high level of ease of navigation, allowing users to complete transactions without technical obstacles. This study concludes that the iterative prototyping approach is effective in producing intuitive application designs that meet the needs of modern society who require flexible laundry services.

Analysis of Student Errors in Solving Problems Involving Curved-Surface Geometric Shapes Based on Newman’s Error Analysis

2026-04-09T10:02:17+07:00

This study aims to evaluate the errors made by students when solving problems involving three-dimensional shapes with curved sides, using Newman’s error analysis approach. The research employed a descriptive qualitative method and was conducted at Imelda Private Junior High School in Medan during the second semester of the 2025/2026 academic year, with the participation of 18 students selected through purposive sampling. Data collection tools consisted of written tests, interviews, and documentation. Data analysis was conducted by referring to Newman’s five stages of error: reading, comprehension, transformation, process skills, and coding.

The research findings indicate that the most common errors were process skill errors, accounting for 25.9%, and transformation errors, accounting for 20.3%, while errors in the reading, comprehension, and coding stages were not identified. Students with low ability typically struggle to find the formula and proceed with the problem-solving process; students with moderate ability tend to make errors during calculations; whereas high-ability students successfully solve problems accurately and in an organized manner.

Thus, it can be concluded that most student errors are caused by an inability to select the correct formula and a lack of precision during calculations. Therefore, it is crucial to implement teaching methods that focus on conceptual understanding and procedural skills to minimize the errors students make when tackling mathematical problems.

Decision Support System Using the Analytical Hierarchy Process Method in Determining Credit Recipient Eligibility

2026-04-10T14:26:54+07:00

Banks play a fundamental role in improving public welfare by collecting funds through savings and redistributing them as credit. Although credit is the primary source of bank revenue, it carries significant risks if the feasibility analysis of prospective borrowers is flawed, potentially leading to non-performing loans that disrupt financial stability. BPR Nusantara Bona Pasogit 17 faces this challenge as it currently lacks an automated decision support system, resulting in assessments that are often inconsistent or subjective. This research aims to develop a web-based decision support system using the Analytical Hierarchy Process (AHP) method to determine credit recipient eligibility. Developed using PHP and MySQL, the system incorporates criteria management, AHP calculation processing, and automated eligibility ranking. Comprehensive validation through black-box and white-box testing confirmed that all functional components performed correctly with consistent "PASS" results. The AHP implementation produced a Consistency Ratio (CR) of 0.03797, indicating high reliability in decision-making. Criterion priority weights were identified as: Income (0.386), Character (0.219), Loan Amount (0.162), Collateral (0.103), Loan Term (0.07), and Age (0.06). System testing on 100 customer records resulted in a maximum eligibility score of 0.93501 and a minimum of 0.41839.

Developing a Web-Based E-Commerce Application for Toko Oleh-Oleh Khas Prabumulih

2026-04-17T14:22:06+07:00

Prabumulih Typical Souvenir Shop is a business unit selling various pineapple-based processed products that still faces constraints in promotional reach and manual transaction efficiency. This study aims to design and build a web-based e-commerce information system as a solution for digital marketing and sales. The system development method used is Rapid Application Development (RAD), consisting of requirements planning, user design, construction, and cutover phases. The application was built using PHP programming language and MySQL database. The results show that the application successfully facilitates online transactions, real-time stock management, and automated sales reporting, which significantly enhances the shop's operational efficiency.

Implementation of Convolutional Neural Network for Emergency Sound Detection for Hearing-Impaired Individuals on Android

2026-04-17T19:21:55+07:00

Hearing impairment is a condition characterized by partial or total loss of hearing ability, which may occur congenitally or be caused by factors such as injury, disease, or prolonged exposure to excessive noise. This study aims to develop an Android-based emergency sound detection system using the Convolutional Neural Network (CNN) method. The research workflow includes problem identification, data collection, data preprocessing, CNN model training, model evaluation, Android application development, and system testing. Experimental results show that the best-performing model achieved an overall accuracy of 93%. The trained model was then implemented into an Android application to enable real-time sound classification and to provide visual notifications when emergency sounds are detected. The evaluation results indicate that the CNN model is capable of accurately classifying emergency sounds and operates effectively on Android devices.

Eye Disease Classification System Based on Fundus Images Using the InceptionV3 Architecture

2026-04-17T19:23:28+07:00

This study aims to develop an automated eye disease classification system based on retinal fundus images using the InceptionV3 deep learning architecture. The dataset consists of four classes: cataract, diabetic retinopathy, glaucoma, and normal, collected from public sources and clinical data. The proposed method applies several preprocessing techniques, including background segmentation, data augmentation, data normalization, and an 80:20 data split to improve model performance and generalization. Transfer learning is implemented by utilizing pretrained ImageNet weights and modifying the final layers to suit the classification task. The model is trained using the Adam optimizer with a learning rate of 0.001 and categorical cross-entropy loss function. Evaluation results show that the model achieves an accuracy of 96%, with average precision, recall, and F1-score values of 0.97, 0.96, and 0.97, respectively. The confusion matrix analysis indicates that most predictions are correctly classified, demonstrating strong performance across all classes. Furthermore, the model is successfully integrated into a web-based system that enables users to upload fundus images and obtain classification results automatically. These findings indicate that the proposed system can effectively assist in early detection of eye diseases and support clinical decision-making.

UI/UX Design of an Incoming and Outgoing Mail Information System using the Design Thinking Method

2026-04-17T22:08:25+07:00

Kantor Inspektorat Kota Jambi saat ini mengelola surat masuk dan keluar secara manual menggunakan buku catatan, yang menyebabkan risiko kehilangan dokumen dan inefisiensi dalam pengambilan data. Penelitian ini bertujuan untuk mendesain Antarmuka Pengguna (UI) dan Pengalaman Pengguna (UX) untuk Sistem Informasi Surat Masuk dan Keluar (SIMAK) yang ramah pengguna untuk meminimalkan kendala operasional tersebut. Metode yang digunakan adalah Design Thinking , yang terdiri dari lima tahapan: empati, definisi, ide, prototipe, dan pengujian. Hasil penelitian ini adalah prototipe aplikasi berbasis web yang dirancang menggunakan Figma, yang menampilkan alat manajemen untuk surat masuk, surat keluar, disposisi, dan laporan. Pengujian yang dilakukan menggunakan metode System Usability Scale (SUS) dengan 10 responden menghasilkan skor rata-rata 89,75 . Skor ini menempatkan desain aplikasi dalam kategori " Diterima " dengan peringkat " Baik ", yang menunjukkan bahwa sistem mudah dipahami dan secara efektif memenuhi kebutuhan pengguna.

Analysis of Mathematics Percentage Calculation Strategies Quickly and Accurately Based on a Literature Review

2026-04-18T14:31:25+07:00

Percentages are one of the important concepts in mathematics that are widely used in various contexts of daily life such as economics, commerce, and decision-making. However, various studies show that the concept of percentage is still a material that is quite difficult for students and prospective mathematics teachers to understand. These difficulties are generally related to the understanding of the basic concept of percentage, the use of the percent symbol (%), and the ability to relate the percentage value to the reference value in a problem. This study aims to analyze various percentage calculation strategies that can be carried out quickly and accurately based on the results of previous research. The method used in this study is a literature study by reviewing various scientific articles from SINTA-accredited national journals and international journals that are relevant to the topic of percentages in mathematics learning. Data is collected through documentation techniques by examining and analyzing research findings related to the percentage calculation strategy. The results of the study show that the use of mental calculation strategies, understanding the basic concept of percentages, and the use of visual models can help improve students' ability to calculate percentages more effectively and efficiently. Therefore, the right calculation strategy is essential to support the understanding of the concept of percentages in mathematics learning.

Sentiment Analysis of Film Audience for IPAR ADALAH MAUT Using Support Vector Machine

2026-04-20T06:59:11+07:00

This study aims to analyze user sentiment on social media X (formerly Twitter) toward the film Ipar Adalah Maut using the Support Vector Machine (SVM) method. The data were collected through a crawling process using the snscrape library, focusing on tweets containing keywords related to the film title. The preprocessing stages included data cleaning, case folding, tokenization, stopword removal, and stemming, while feature extraction was performed using Term Frequency Inverse Document Frequency (TF-IDF). Sentiment was classified into two categories, namely positive and negative, using the SVM algorithm. The results showed that the model achieved 100% accuracy on the training data and 82% accuracy on the testing data, indicating good generalization performance, although there is a potential risk of overfitting due to the gap between training and testing results. These findings demonstrate the effectiveness of SVM in analyzing sentiment related to film discussions on social media and provide a basis for future research by incorporating larger and more balanced datasets.

Flood Prediction for the Wampu River Basin Using the Simple Additive Weighting Method:A Case Study of the Wampu River in Bahorok

2026-04-21T12:13:23+07:00

Flood is one of the natural disasters that frequently occurs in the Wampu Watershed (DAS Wampu), especially in Bahorok District. Flood risk is influenced by several factors such as rainfall, slope gradient, land use changes, and river depth. The problem in this study is the absence of a decision support system that can objectively determine flood risk levels. This study aims to determine the criteria and weights of flood risk, apply the Simple Additive Weighting (SAW) method, and analyze the accuracy level of the SAW method in determining flood risk. The method used in this research is the Simple Additive Weighting (SAW) method through several stages including criteria weighting, decision matrix construction, data normalization, preference value calculation, and alternative ranking. The research data consists of 18 villages with four criteria: rainfall, slope gradient, land use change, and river depth. The results show the classification of flood risk levels into high, medium, and low categories based on the obtained preference values. Villages with the highest preference values indicate a higher level of flood vulnerability compared to other villages. The model evaluation results indicate that the SAW method has an accuracy level of approximately 90% in determining flood risk classification. Based on these results, it can be concluded that the SAW method can be used as a decision support system to determine flood risk levels and provide recommendations for priority flood mitigation areas in Bahorok District.

UI/UX Design of an Android-Based Sales Application at Naureen Shop using the User-Centered Design Method

2026-04-22T14:20:29+07:00

The development of digital technology has shifted consumer behavior to prioritize convenience and efficiency through online shopping. Naureen Shop, a women's clothing business, currently faces operational constraints due to manual sales, promotion, and data recording processes, resulting in sub-optimal service. This study aims to design the User Interface (UI) and User Experience (UX) of an Android-based sales application for Naureen Shop to enhance business effectiveness. The method employed is User Centered Design (UCD), a design approach focusing on user needs and characteristics through stages of identifying the context of use, specifying user requirements, creating design solutions using Figma, and evaluation. The design testing results using the System Usability Scale (SUS) method with 15 respondents yielded an average score of 77.0. This score indicates that the application design falls into the "Acceptable" category with a "Good" Adjective Rating. Consequently, the resulting design solution fulfills functional aspects and provides a satisfying user experience to support transaction processes at Naureen Shop

Application of the Tsukamoto Fuzzy Inference System Method for Rainfall Prediction in the Adolina Area

2026-04-23T15:06:07+07:00

Rainfall is one of the key elements in the climate system that significantly affects various sectors, such as agriculture, spatial planning, and disaster mitigation. Adolina, a region with tropical weather characteristics and highly fluctuating rainfall, requires an accurate prediction system to support informed decision-making. This study applies the Fuzzy Inference System (FIS) Tsukamoto method to predict rainfall based on input variables such as air temperature, humidity, and wind speed. The Tsukamoto method is chosen for its capability to handle uncertainty and produce crisp output values through inference and defuzzification processes based on a set of fuzzy rules. The results show that the Tsukamoto FIS provides reasonably accurate and consistent rainfall predictions with a low error rate. Therefore, this approach can serve as an effective alternative in weather decision-support systems for the Adolina area.

Implementation of Simple Queue and Content Filtering for Bandwidth Management on WLAN and LAN Networks

2026-04-24T09:29:48+07:00

SMK Negeri 1 Sungai Raya is a vocational high school located on Sultan Agung Street, Kuala Dua, Sungai Raya District, Kubu Raya Regency, West Kalimantan, which offers programs such as Visual Communication Design (DKV) and Broadcasting that utilize the internet to support learning activities including completing assignments, accessing educational resources, and submitting schoolwork; however, problems frequently occur in the laboratory network such as buffering, network downtime, and bandwidth congestion due to simultaneous usage, therefore bandwidth management using the simple queue method was implemented along with content filtering to block access to social media and online gaming websites in order to prevent disruptions to the learning process, and the results showed improvements in network performance where on the LAN network throughput decreased by 0.5735%, packet loss decreased by 0.0969%, delay decreased by 0.5942%, and jitter decreased by 0.9182%, indicating better stability and efficiency, while the WLAN network in the laboratory was also successfully installed, providing improved connectivity and supporting a more effective and focused learning environment.

Design and Construction of a Village Tourism Monitoring and Evaluation System Web Based

2026-04-30T11:03:00+07:00

Development of village tourism in the Special Region of Yogyakarta requires structured and sustainable management, particularly in monitoring and evaluation as a basis for stakeholder decision-making. Current challenges include unintegrated tourism village data, manual evaluation processes, and limited public access to tourism information and services. This study aims to design and develop a web-based monitoring and evaluation system that integrates registration, data management, monitoring, scoring, and information presentation in a centralized platform. The system is developed using the Extreme Programming method, which includes planning, design, coding, and testing stages, with functional testing conducted through Black Box Testing. The technologies used include React JS for the interface, Express JS for the backend, Supabase as the database, and Google Cloud Storage for data storage. The results indicate that all main system features function according to requirements, supporting more effective monitoring and evaluation processes, improving data accuracy, and enhancing accessibility of information for both the public and local government. Furthermore, this system has the potential to serve as a foundation for regional tourism data integration and to support sustainable tourism village development policies, while also contributing practically to improving integrated digital public information services at the national level.

Chinese Script Handwriting Pattern Introduction Application Design with Algorithm CNN-SVM

2026-04-25T09:08:08+07:00

The Chinese script has a high level of visual complexity because each character consists of thousands of intricate strokes. This is a big challenge for second-language learners, especially in recognizing the various variations of human handwriting. This study aims to design an accurate and efficient application for the recognition of Chinese handwriting patterns based on Android using a hybrid model of Convolutional Neural Network (CNN) and Support Vector Machine (SVM). In this system, the CNN works like a human eye that distinguishes the details of the shape of an image, while the SVM serves as the brain that decides what characters are being written. The data used in the training process included 7,330 Chinese characters pulled from the Kaggle platform. The results of the study show that the application was successfully designed and able to display character shapes, how to read (pinyin), and the meaning of words offline without the need for an internet connection. Based on testing the Black Box method, all of the app's features are proven to work validly. The study concluded that the use of the CNN-SVM hybrid model was highly effective in recognizing diverse handwriting variations, although the degree of accuracy remained dependent on the clarity of the quality of the images taken by the user.

Classification of Herbal Plants Based on Leaf Images Using Gray Level Co-Occurrence Matrix and K-Nearest Neighbor

2026-04-30T10:53:17+07:00

Herbal plants have long been used as traditional medicine. However, many people struggle to tell different herbal leaves apart because they look quite similar. This study tries to build a system that can recognize two types of herbal leaves, Moringa and Katuk, simply from their photos. We used GLCM to extract texture features from the leaves, then classified them using KNN. The dataset came from Kaggle, with 480 leaf images in total. Before processing, we cropped the images, resized them to 256x256 pixels, and converted them to grayscale. GLCM features were taken from four angles (0°, 45°, 90°, 135°) and then averaged. This gave us four texture values: contrast, correlation, energy, and homogeneity. We tested KNN with k values from 1 to 15 and five different distance metrics. The best result we got was 94% accuracy, using Manhattan distance with k=1. This system could help everyday people identify medicinal plants more easily without needing lab tests.

Student Mental Health Monitoring System Based on Daily Activities with the SVM Method

2026-04-28T13:46:01+07:00

Student mental health is a crucial issue that requires effective and responsive self-monitoring systems. This study aims to develop "LacakJiwa," an Android-based mobile application designed to monitor student mental health through the analysis of daily activity patterns. The method employed is the Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel to classify mental health risks into low and high categories. Input data includes sleep duration, daily step count, gadget usage, and social interaction duration collected from 146 student data entries. The SVM model is integrated into the application using TensorFlow Lite to enable on-device classification, ensuring user privacy through SQLite local database storage. Testing results on 44 test samples showed an accuracy rate of 52.27%, precision of 36.36%, and recall of 22.22%. While the system was successfully implemented technically, the low recall value indicates significant challenges in detecting complex non-linear behavioral patterns in students. This research provides a foundation for developing digital self-control instruments that are adaptive to Indonesian local culture.

Comparative Analysis of Sobel, Prewitt, and Canny Methods in Detecting Object Edges in Betta Fish Images

2026-04-30T10:51:42+07:00

Edge detection is a crucial stage in digital image processing for recognizing the shape and structure of an object. The application of edge detection to betta fish images presents a unique challenge due to their layered, intricately textured, and often semi-transparent fin morphology. This study aims to analyze and compare the performance of three edge detection algorithms, namely Sobel, Prewitt, and Canny, in extracting shape features from betta fish images. The research methodology involved converting the dataset images into a grayscale format and subsequently implementing the three algorithms using the OpenCV library in the Python programming language. The evaluation was conducted visually by observing the sharpness of the edge lines, object continuity, and the occurrence of noise. The results indicate that the Canny algorithm provides the most optimal performance, as it is capable of detecting the thin edge lines of the fish fins with greater detail and continuity due to its hysteresis thresholding process. Meanwhile, the Sobel and Prewitt methods produced thicker edge lines but were less sensitive to the details of the transparent fins. This study is expected to serve as a reference in selecting the appropriate segmentation method for biological objects with complex morphologies.

Smart Absen Implementation of a Facial Recognition-Based Student Attendance System Using the Haar Cascade Method and LBPH

2026-05-02T19:38:06+07:00

Manual attendance systems in higher education institutions are often hampered by inefficiency, data inaccuracy, and vulnerability to fraud such as proxy attendance. This study presents the design and implementation of Absen Smart, a face recognition-based attendance system developed using the Haar Cascade and Local Binary Pattern Histogram (LBPH) algorithms within the React.js and Flask frameworks. This system enables the automatic and real-time identification of students via a webcam without requiring additional hardware. Face detection is performed using the Haar Cascade classifier from OpenCV, while face recognition uses the LBPH Face Recognizer with a confidence threshold of 50. Testing was conducted with 28 registered students from the Computer Science Program at UNIMED, Class A, 2024 cohort. Functional evaluation results show that all seven core system features—including face detection, face recognition, duplicate prevention, automatic absence tracking, and Excel report generation—were successfully executed with a 100% success rate. The system achieved a facial recognition accuracy of 92.86%, with an average processing time of 1.2 seconds per verification. These results indicate that the proposed system is an effective, practical, and scalable solution for automating academic attendance in a university setting.

Implementing the Procedural Generation Method for Placing Dynamic Objects in a Roblox-Based Adventure Game

2026-05-02T19:44:56+07:00

Procedural generation is a content-creation technique that has become increasingly important in modern game development. However, on the Roblox platform, dynamic object placement still faces challenges such as overlapping, illogical positioning, and blocked navigation paths when relying solely on pure random methods. This research implements the Rule-Based Random Generation algorithm to manage the automatic placement of dynamic objects (enemies, treasure chests, and traps) in a Roblox-based adventure game. The proposed method combines randomization with constraint validation, including boundary check, overlap check using Euclidean distance, restricted zone check, and cross-type relational constraints. The system was developed in Roblox Studio with the Luau scripting language using a prototyping methodology and a modular architecture comprising ObjectSpawner, ConstraintValidator, SpatialGrid, and DungeonGenerator. Functional testing was conducted across 10 game sessions on a 1000 × 1000 studs map with a configuration of 340 enemies, 10 chests, and 50 traps. The results show that the system successfully placed all objects without any constraint violation, produced significant spatial variation between sessions (ranging from 86.31 to 2358.00 studs), and maintained level playability in every session. The average spawning execution time was 336.62 ms per session (0.84 ms per object), demonstrating the computational efficiency of the proposed method.

Recommendation System for Selecting Maternity Hospitals in Pontianak using Weighted Product Method

2026-05-02T19:47:38+07:00

In this research, a decision support system for recommending the selection of maternity hospitals in Pontianak was developed using the Weighted Product (WP) method, with the constructed system in the form of a web-based application. The aim of this study is to facilitate pregnant women in choosing maternity hospitals in Pontianak based on criteria obtained from a survey of pregnant women, including distance, facilities, cost, and reputation. The WP method was applied through three main stages: weight normalization, vector S calculation, and vector V computation for final ranking. Testing in this research involves five alternative maternity hospitals, and each criterion is assessed on indicators ranging from 1 to 5. The results obtained indicate that Anugerah Bunda Khatulistiwa Maternity Hospital achieved the highest final ranking score among all evaluated alternatives. This system is expected to assist expectant mothers in making more informed decisions when selecting a maternity hospital that best suits their needs.

Sentiment Analysis on Electric Vehicles in Indonesia Using Bidirectional Encoder Representations from Transformers (BERT) and Named Entity Recognition (NER) Methods

2026-05-07T08:50:51+07:00

Air pollution is a major environmental issue due to its significant impact on human health, with the transportation sector being one of the largest contributors. In Indonesia, increasing motor vehicle usage has led to higher greenhouse gas emissions, encouraging the transition toward electric vehicles as a cleaner alternative. However, the adoption of electric vehicles is influenced not only by technical factors such as infrastructure and cost, but also by public perception, which varies across different digital platforms. This study aims to analyze public sentiment toward electric vehicles in Indonesia using a Natural Language Processing (NLP) approach by combining Bidirectional Encoder Representations from Transformers (BERT) and Named Entity Recognition (NER). BERT is utilized to classify sentiments into positive, negative, and neutral categories by considering bidirectional contextual information, while NER is used to identify key entities such as companies, products, locations, and issues discussed in public discourse. The results show that the BERT model achieves an accuracy of 71.05%, precision of 61.31%, recall of 59.28%, and a misclassification error of 28.95%, indicating a fairly good performance in sentiment classification. Furthermore, NER analysis reveals that event and opinion are the most influential factors affecting public interest, followed by company, product, and quality, while location, price, action, and feature have lower influence. Overall, public interest in electric vehicles in Indonesia is relatively high but dynamic, as it is strongly influenced by circulating information and public opinion.

Implementation of the Gradient Boosting Algorithm for Palm Oil Price Prediction

2026-05-06T14:07:16+07:00

The price of palm oil is highly volatile due to the influence of global market dynamics, trade policies, and climate change, creating uncertainty for industry players in decision-making. This research aims to implement the XGBoost (Extreme Gradient Boosting) algorithm, optimized using Grid Search Cross-Validation, to predict palm oil prices. The dataset used is the Palm Oil Futures Historical Data.csv obtained from Kaggle, consisting of nine features. Data preprocessing is performed using StandardScaler for normalization, followed by model training with hyperparameter tuning. The system is built as a web-based application separating the frontend using PHP and Flask as the Backend API. Testing on 105 test data points yielded an MAE of 43.97, RMSE of 65.14, and R² of 91.82%, demonstrating the model’s strong ability to explain palm oil price variation. Based on the results, the XGBoost algorithm is suitable as a decision-support tool for commodity price prediction, achieving high accuracy consistent with standard criteria for commodity price forecasting and capable of handling large datasets.

Implementation of the Random Forest Algorithm for Loan Eligibility Prediction and Feature Analysis Based on Financial Data

2026-05-06T12:41:32+07:00

The advancement of information technology has led to an increasing demand for loan access, both through banking institutions and online lending platforms. However, the process of evaluating loan eligibility, which is still carried out manually or semi-manually, is prone to human error and decision-making bias, ultimately increasing the risk of loan defaults. This study aims to implement the Random Forest algorithm to predict loan eligibility based on financial data, as well as to evaluate its accuracy. The dataset used in this study is loan_approval_dataset.csv, which is downloaded from Kaggle, utilizing 11 input features. The system is developed as a web-based application using Laravel as the main frontend and backend framework, while Flask is used as a backend API for executing the machine learning processes. The testing results show that the Random Forest model achieves an accuracy of 98.44%, with a precision of 98.14%, recall of 99.37%, and an F1-score of 98.75%. Furthermore, the cibil score feature is identified as the most influential factor in the prediction process, contributing 80.65% to the model's outcome. These findings indicate that the Random Forest algorithm is highly effective for use in a loan eligibility prediction system, as it provides fast, objective, and highly accurate results.

Influencing the Success of SPBE Jambi Provincial Government Using the SEM Method

2026-05-08T11:12:47+07:00

The Electronic-Based Government System (SPBE) is a strategic instrument for digital governance in Indonesia, but its success in local government remains uneven. The problem addressed in this study is the fluctuating SPBE performance of the Jambi Provincial Government, which is associated with network instability, changing application coordinators, overlapping data input, diverse user age groups, and uneven digital literacy among state civil apparatus (ASN) and service users. This study aims to identify the determinants of SPBE success from the ASN perspective and to explain how system quality and information quality shape perceived ease of use, perceived usefulness, user satisfaction, and net benefits. A quantitative explanatory survey was conducted with 385 ASN respondents who interact with SPBE services in the Jambi Provincial Government. The research model integrates constructs from technology acceptance and information system success perspectives and was tested using partial least squares structural equation modeling. The measurement results show that all indicators are valid, with outer loading values above 0.70, AVE values above 0.50, and Cronbach alpha values above 0.80. The structural results indicate that information quality has the strongest effect on perceived usefulness (beta = 0.861), followed by user satisfaction on net benefits (beta = 0.844) and system quality on perceived ease of use (beta = 0.834). Perceived usefulness also has a stronger effect on user satisfaction than perceived ease of use. These findings confirm that SPBE success in Jambi depends primarily on accurate, complete, timely, and relevant information that creates real work benefits and sustained user satisfaction.

Effective Strategies for Memorizing Mathematical Formulas in a Literature Review Study

2026-04-25T23:26:38+07:00

Mathematics is often perceived as a difficult subject due to the large number of formulas that students must understand and memorize. This condition can lead to learning difficulties and trigger mathematics anxiety, which may reduce students’ ability to retain mathematical concepts. This study aims to examine various effective strategies for memorizing mathematical formulas based on previous research. The research employed a qualitative approach using a literature review method involving 30 relevant scientific articles obtained from several academic databases. Data were collected through a literature study, while the analysis was conducted by identifying, comparing, and synthesizing findings from the collected literature. The results show that strategies for memorizing mathematical formulas can be categorized into four main groups: audio-musical and artistic strategies, digital technology and gamification innovations, cognitive strategies through mnemonics and kinesthetic tools, and structured drill methods. These strategies have been shown to improve memory retention, learning motivation, and students’ learning outcomes. The findings indicate that the application of creative and multisensory learning strategies can help students memorize mathematical formulas more effectively.

Analysis of Taxsee Driver User Satisfaction in Jambi City Using the Servqual Method

2026-05-02T19:29:42+07:00

This research analyzes driver satisfaction levels in Jambi City using the Taxsee Driver application through the Service Quality (SERVQUAL) method. The study background is user complaints regarding GPS inaccuracies and the automatic order system that affects driver performance ratings. Data were collected via online questionnaires from 385 active drivers in Jambi City and processed using Structural Equation Model with SmartPLS. Results show that three of five SERVQUAL dimensions significantly affect user satisfaction: Tangibles (T-Statistic 5.073), Responsiveness (T-Statistic 3.782), and Empathy (T-Statistic 4.026). Reliability (T-Statistic 1.735) and Assurance (T-Statistic 1.303) were not significant. The R-Square value of 0.879 indicates the model explains 87.9% of user satisfaction variation. Developers are recommended to improve navigation accuracy and responsiveness to maintain driver partner loyalty.

Implementation of a Web-Based Decision Support System for New Employee Recruitment Using the VIKOR Method

2026-04-28T17:45:44+07:00

An effective and objective employee selection process is essential to obtain high-quality human resources. This study aims to develop a web-based decision support system to assist in the recruitment of new employees using the VIKOR method. The VIKOR method is chosen because it can rank alternatives based on their closeness to the ideal solution while considering compromise among criteria. The criteria used in the system include education, work experience, skills, interview results, and work personality. This research adopts the waterfall approach for system development and implements PHP programming language with a MySQL database. The testing results indicate that the system is capable of providing accurate and consistent rankings of job candidates, as well as facilitating the HR team in conducting evaluations more efficiently.

Design of 3D Puzzle Game "Moodoria" Using Unity as an Educational Media for Emotional Intelligence

2026-05-07T08:17:15+07:00

Emotional awareness is crucial for mental health, yet conventional education methods are often less engaging for adolescents. This study aims to design and develop a 3D puzzle game called "Moodoria" using Unity as an interactive medium for emotional intelligence education. The research method used is Research and Development (R&D) with the Game Development Life Cycle (GDLC) model, including needs analysis, literature study, concept design, implementation, and user testing. 3D assets were created using Blender. The game was tested on a small group of users (5–10 people) using a Likert-scale questionnaire. Results show that all main features (menu navigation, character movement, object interaction) function well. User assessments scored high on gameplay (4.87 for challenge) and enjoyment (4.67), and the game was considered feasible as an emotional education medium (average score 4.23). In conclusion, "Moodoria" was successfully developed as an engaging educational game, although sound effects and character expression variations need improvement.

Classification of Handwriting Margin Patterns Using Ensemble Bagging Decision Tree

2026-05-05T20:05:05+07:00

Analysis of handwriting margins plays an important role in graphology, as margin patterns are often associated with individual behavioral tendencies and personality traits. Therefore, detecting and classifying margin characteristics is essential to support automated handwriting analysis using computational approaches. This study uses a computer vision-based approach to classify left-margin handwriting patterns into widening and narrowing categories. The classification is performed by analyzing margin characteristics extracted from scanned handwriting images. The processing pipeline consists of image preprocessing, hybrid feature extraction, and classification using an Ensemble Bagging Decision Tree model. The preprocessing stage enhances image quality through grayscale conversion, contrast adjustment, adaptive thresholding, and noise removal, followed by Region of Interest extraction to focus on the handwriting area. The feature extraction stage applies a hybrid strategy that combines line-based margin analysis and global spatial features to capture both local variations and overall structure. Model performance is evaluated using stratified 5-fold cross-validation to ensure reliable and unbiased results. The experimental findings show that the method achieves an average accuracy of 84.91%, with relatively balanced precision, recall, and F1-score across both classes. These results indicate that margin-based features are effective for representing handwriting patterns in classification tasks. However, variations in writing style and noise from the scanning process may influence performance. Overall, this study demonstrates that the applied approach provides reliable classification results and has potential for further improvement through feature expansion and more advanced learning models.

Sentiment Analysis of SPayLater and SPinjam Features in the Shopee Application Using the Support Vector Machine (SVM) Algorithm

2026-05-08T12:40:36+07:00

The rapid development of information technology and the increasing use of e-commerce applications have generated a large number of user reviews that can be used to measure user satisfaction. SPayLater and SPinjam, as features in the Shopee application, receive various responses in the form of positive, negative, and neutral sentiments, making automatic sentiment analysis necessary. This study aims to analyze user sentiment and implement the Support Vector Machine (SVM) algorithm to classify reviews. The data used consist of 500 user reviews obtained from the Google Play Store. The method includes preprocessing, labeling, and classification using SVM. The results show that there are 231 positive, 230 negative, and 39 neutral sentiments. Model evaluation yields an accuracy of 74%, precision of 0.78, and recall of 0.84, indicating that the model performs fairly well. The developed system is also capable of processing data automatically and displaying classification results effectively. Therefore, the SVM algorithm is effective for sentiment analysis of SPayLater and SPinjam services in the Shopee application.

Analysis of Green Computing Implementation Strategies for Energy Efficiency in Server Infrastructure

2026-05-09T12:03:34+07:00

The rapid development of information technology has increased the use of server infrastructure in various organizations and data centers, resulting in higher energy consumption and operational costs. Green computing is considered an effective approach to improve energy efficiency while reducing environmental impacts. This study aims to analyze green computing implementation strategies for improving energy efficiency in server infrastructure. The research used a descriptive qualitative method through literature studies and comparative analysis of energy management strategies in data centers. The strategies analyzed include server virtualization, server consolidation, energy-efficient hardware, and cooling system optimization. The results indicate that the implementation of green computing can significantly reduce energy consumption compared to conventional server systems. In addition, the implementation improves operational efficiency, reduces electricity usage, and supports environmentally sustainable data center management. Therefore, green computing can be considered an effective solution for developing efficient and environmentally friendly server infrastructure.

Implementation of Random Forest Algorithm for Classifying Land and Building Tax Arrears and Risk Factor Analysis Dashboard

2026-05-09T22:49:08+07:00

This study aims to develop a predictive model to identify the potential for land and building tax arrears and analyze the dominant risk factors contributing to non-compliance. The research utilizes the Random Forest classification algorithm applied to historical tax data from the Regional Financial and Revenue Management Agency of Binjai City. The approach involves data preprocessing, feature engineering including target encoding for geographical areas, and model training with hyperparameter tuning to optimize classification performance. Furthermore, a web-based interactive dashboard is developed using the Flask framework to visualize the predictions and risk factors. The results demonstrate that the Random Forest model achieves a robust and consistent accuracy of approximately 85% in classifying compliant and non-compliant taxpayers. Feature importance analysis reveals that land area is the most dominant risk factor influencing tax arrears, significantly outweighing other variables. In conclusion, the integration of the Random Forest algorithm with an interactive dashboard provides a highly accurate, efficient, and scalable solution for local governments to transition from reactive tax collection to proactive, data-driven risk management.

Design of a Multi-Tenant Waste Management System with Volume Estimation and Vehicle Trip Optimazation

2026-05-09T15:34:42+07:00

Waste management at the village level still faces a number of challenges, such as unstructured waste volume recording, suboptimal collection scheduling, and a lack of transparency in cost management. This study aims to design a multi-user waste management system equipped with a volume estimation model and vehicle route optimization. The approach applied includes a literature review to analyze system requirements, followed by design using flowcharts, Data Flow Diagrams (DFDs), and Entity-Relationship Diagrams (ERDs). The research findings indicate that the developed system successfully integrates the management of waste source data, transportation processes, and cost calculations in a structured manner. The volume estimation model is used to estimate the amount of waste in the field, while route optimization determines the number of vehicle trips based on their carrying capacity. Additionally, the multi-tenant concept allows this system to be used by various regions simultaneously while ensuring data separation. Therefore, this system is expected to improve operational efficiency, management transparency, and the quality of waste transportation services.

Application of Data Mining using the Apriori Algorithm in Analyzing Subject Selection Patterns of Tutoring Students

2026-05-09T15:35:23+07:00

This study examines the application of data mining using the Apriori algorithm to analyze subject selection patterns among tutoring students in Kudus, Central Java. With the increasing number of students attending tutoring, understanding subject selection patterns is crucial to improve the effectiveness of educational services. The Apriori algorithm, a popular association rule mining technique, is used to identify relationships between frequently selected subjects. The research dataset consists of student subject selection transaction data, including information such as student name, student ID number, tutoring branch, and selected subjects. The analysis process included data preprocessing, data transformation into transaction format using Transaction Encoder, application of the Apriori algorithm with a minimum support of 0.05, and formation of association rules with a minimum confidence of 0.3. The results show frequent itemsets indicating the most popular subjects and association rules that describe students tendencies in selecting subject combinations. These findings can be utilized by tutoring managers to design more effective learning packages, optimize the allocation of teaching resources, and provide subject recommendations tailored to student needs. This research contributes to the development of educational data mining in the context of tutoring institutions in Indonesia.

Comparative Analysis of K-Means Clustering and K-Medoids Clustering Methods in Clustering Neonatal Infant Mortality Rates in West Java Province

2026-05-09T15:36:03+07:00

Neonatal mortality rate is an important indicator in assessing public health conditions. This study aims to cluster neonatal mortality data in West Java Province using the K-Means Clustering and K-Medoids Clustering methods, as well as compare the performance of both methods in producing the best clusters. The study used secondary data obtained from Open Data West Java. The research stages included data selection, preprocessing, clustering, and evaluation using the Davies-Bouldin Index (DBI). The experiments were conducted using cluster variations (k) from 2 to 8. The results showed that the K-Means Clustering method produced the best performance with a DBI value of 0.430 at k = 3. The clustering results generated three categories: low-risk cluster with 408 data points, medium-risk cluster with 65 data points, and high-risk cluster with 13 data points. The differences in cluster characteristics indicate variations in neonatal mortality risk levels among regions in West Java Province. The findings of this study are expected to support decision-making and more targeted health policy planning.

Keywords: K-Means Clustering, K-Medoids Clustering, Davies-Bouldin Index, Neonatal Mortality.

Analysis and Design of the Nusa Graha Module for Village Asset Management and Facility Booking on the NUSAEKA Multi-Tenant SaaS Platform

2026-05-13T12:37:56+07:00

In most regions of Indonesia, village asset management and the process of booking village facilities are still carried out manually, which can lead to disorganized record-keeping, data loss, and a lack of access for village residents. This study was conducted to analyze and evaluate the Nusa Graha module as a component of the Nusaeka multi-tenant SaaS platform, focusing on village inventory management, automatic asset depreciation, and web-based village booking services. This research was conductes through a literature review and system analysis obtained through consultation with supervising lecturer as well as document analysis. The analysis results include business flowcharts, Data Flow Diagrams (DFDs) at levels 0 and 1, and Entity-Relationship Diagrams (ERDs), which consist of several main tables. The research findings indicate that the Nusa Graha module can support and streamline asset management and the structured process of facility rentals using multi-tenant data via tenant_id and a modular language. Additionally, the Nusa Graha module facilitates integration with the Nusa Artha financial module if the village subscribes to it.

Customers’ Loss of Confidence in Banking Security Systems: A Case Study of the Loss of BRI Customers’ Funds

2026-05-13T17:06:27+07:00

The phenomenon of customer funds going missing in the banking sector, particularly in the case of Bank Rakyat Indonesia (BRI), has raised concerns about the security of the digital banking system and has led to a decline in public confidence. This study aims to analyse the crisis of customer confidence in banking security systems by examining influencing factors, such as cyber risk, risk perception, and the role of social media. The research method employed is a qualitative approach using case studies, utilising secondary data obtained from academic journals, institutional reports, and case documentation.
The research findings indicate that the loss of customer funds is influenced by vulnerabilities in digital security systems and the rise in cybercrime, such as phishing and social engineering. Furthermore, these incidents have led to a decline in customer trust, a trend exacerbated by the dissemination of information via social media.
This study concludes that the crisis of customer trust is caused not only by technical factors, but also by risk perceptions and the dynamics of public information. Therefore, improvements in banking system security, strengthened consumer protection, and effective communication strategies are required to maintain customer trust.

Design of a Web Based Population Data Information System at Matawai Atu Village Office

2026-05-12T15:24:47+07:00

The development of information technology has greatly influenced many sectors, including village administration. The Matawai Atu Village Office, located in Umalulu Subdistrict, East Sumba Regency, still uses a manual system to record population data such as births, deaths, new residents, and relocations. Data is recorded in a main register book and then processed using Microsoft Word to create reports. This method causes several problems, including the risk of data loss, data entry errors, slow data searching, and delays in report preparation. To solve these problems, this study aims to design a web-based population data information system that is effective and efficient. The study uses the Waterfall method, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance. The system is developed using PHP and a MySQL database. Data collection is carried out through interviews, direct observation at the research location, and literature study. System testing is conducted using Black Box Testing to ensure that all features work properly, and the System Usability Scale (SUS) to measure how easy the system is for users. The results show that the developed system can manage population data more accurately, quickly, and securely. The system also makes it easier for staff to search data, manage documents, and prepare reports. With this system, it is expected that public services at the Matawai Atu Village Office will improve and better support the work of village staff.

Implementation of a Chatbot-Based AI Agent for Employee and Student Attendance Systems with Face Recognition and N8N Integration

2026-05-13T12:31:45+07:00

Students frequently rely on direct messaging to verify the presence of lecturers and staff on campus, a practice that often results in delayed responses due to the recipients' busy schedules. This study aims to design, implement, and evaluate an automated attendance system based on an AI agent utilizing face recognition technology and n8n as a centralized workflow automation platform. The research employs a Research and Development (R&D) approach with the Agile development method. Real-time face detection and recognition are performed from CCTV camera feeds using a Python module that integrates the InsightFace and MediaPipe algorithms. Identified attendance data is automatically stored in Google Sheets, subsequently processed by n8n to deliver information to users via a WhatsApp chatbot powered by the Gemini 2.5 Flash model. Testing conducted on 419 samples yielded an accuracy of 86.16%, with 275 True Negative values demonstrating the system's capability in filtering unregistered faces. The overall average system latency was 15.9 seconds, with a chatbot automation response time of only 9.3 seconds. This research demonstrates that the integration of workflow automation and AI agents is effective in improving the efficiency of academic attendance information access.

Selection of Outstanding Lecturers Using the Simple Multi-Attribute Rating Technique (SMART) Method

2026-05-13T12:35:31+07:00

Lecturers play a crucial role as professional educators in the implementation of higher education through the Tridarma Perguruan Tinggi (Triple Dharma of Higher Education), which encompasses education, research, and community service. The selection of exemplary lecturers serves as both a form of recognition and a motivational instrument to enhance institutional quality. However, the selection process is often hindered by subjective assessments and the lack of standardized measurement, which may lead to dissatisfaction and diminish the objectivity of the results. This study aims to address these issues by implementing a Decision Support System (DSS) using the Simple Multi-Attribute Rating Technique (SMART) method. The SMART method was selected for its effectiveness in facilitating multi-criteria decision-making through weight assignment to priority parameters, such as scientific publications, educational qualifications, and external achievements. The results of this system implementation are provide structured, transparent, and accurate decision recommendations, ensuring that the selection of exemplary lecturers is conducted objectively based on measurable data.

Design of an Android-Based Sitting Posture Detection Application Using Deep Learning

2026-05-14T14:19:29+07:00

Prolonged poor sitting posture is a major cause of musculoskeletal disorders including lower back pain and spinal abnormalities. This study designs and implements PosturApp, a deep learning-based Android application for real-time sitting posture detection using Kotlin. A Multi-Layer Perceptron (MLP) model was trained on 3,526 keypoint datasets sourced from the Kaggle public dataset (Posture Recognition) and direct image capture using an Android front camera, extracting 66 coordinate values from 33 body landmarks via MediaPipe BlazePose. The model was converted to TensorFlow Lite (TFLite) format at approximately 78 KB for on-device inference without internet connectivity. Evaluation results show an accuracy of 97.81% with precision 0.99, recall 0.99, and F1-Score 0.98. The application provides real-time visual feedback through interface color changes and corrective notifications, along with a gallery-based classification feature. Functional testing across eight posture scenarios yielded entirely correct results with confidence values ranging from 59% to 99%.

Design of a Warehouse Inventory Management System Using FEFO Method in NUSA Niaga Multi-Tenant

2026-05-15T09:15:04+07:00

Most village-based businesses still manage their inventory manually, from monitoring warehouse stock and generating reports and checking items based on expiration dates. This process is considered inefficient and carries the risk of errors in data recording and reporting. In the process of transferring inventory to the display on the web-based NUSA Niaga platform, the FEFO method is applied. Needs analysis, system design, design implementation, and system documentation were conducted. Literature review on management systems, the FEFO method, multi-tenant architecture, and RBAC were used for data collection. The system was designed to monitor inventory, manage products nearing expiration, record goods transfers, and implement multi-tenant functionality using flowcharts, ERDs, and DFDs. This system is expected to help manage BUMDes warehouse in a more connected and structured manner.

Analysis of JKN Mobile User Satisfaction using SVM and KNN Methods Through PSO Optimization

2026-05-18T12:44:10+07:00

This study was conducted to evaluate the service quality of the JKN Mobile application developed by the Health Social Security Administering Agency (BPJS Kesehatan) as a means of facilitating participants in accessing health services. Although the application provides convenience for users, there are still various complaints indicating that the service is not running optimally. Therefore, this study aims to analyze the positive and negative sentiments of JKN Mobile application users by comparing the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms based on Particle Swarm Optimization (PSO). The research method was carried out by processing user review data using sentiment classification techniques. The test results showed that the SVM algorithm obtained an accuracy of 85.02% with an AUC value of 0.815, while the PSO-based SVM increased to 86.71% with an AUC of 0.831. The KNN algorithm obtained an accuracy of 39.54% with an AUC of 0.500, while the PSO-based KNN increased to 87.05% with an AUC of 0.736. The results of the study prove that the implementation of PSO is able to improve the accuracy performance of both algorithms.

The Effect of Mobile Banking Usage on Banking Customer Satisfaction

2026-05-16T23:34:44+07:00

The rapid development of information technology has encouraged the banking sector to innovate through digital-based services, one of which is mobile banking. This service provides convenience for customers in conducting banking transactions quickly, effectively, and efficiently through smartphones without having to visit bank offices. This study aims to determine the effect of mobile banking usage on customer satisfaction in banking services. The research method used is a quantitative descriptive approach with data obtained from previous journals and supporting literature related to mobile banking and customer satisfaction. The results indicate that the use of mobile banking has a positive and significant effect on customer satisfaction. Factors such as ease of use, transaction speed, security, service quality, trust, and digital banking innovation are proven to influence customer satisfaction in using mobile banking services. In addition, digital banking services are able to improve customer convenience and efficiency in carrying out financial transactions. Therefore, banks are expected to continuously improve the quality of digital services, strengthen transaction security systems, and provide sustainable innovations to maintain customer satisfaction and loyalty in the digital era.

Evolution and Impacts of AI-Based Rainfall Prediction Systems on Agricultural Management in Tropical Regions: A 20-Year Systematic Review

2026-05-18T12:37:11+07:00

Global climate change has significantly disrupted rainfall patterns in tropical regions, posing major challenges to agricultural productivity and food security. Accurate rainfall prediction has become a critical component of data-driven agricultural management. This study conducts a systematic literature review (SLR) following the PRISMA 2020 guidelines to analyze the evolution of AI-based rainfall prediction systems and their multidimensional impacts on tropical agricultural management over the period 2008–2026. Data were sourced from Scopus using three Boolean search strings, yielding 239 records, of which 235 articles were retained after duplicate removal and quality assessment using the Mixed Methods Appraisal Tool (MMAT) with a threshold score of ≥5. Bibliometric analysis was conducted using VOSviewer and Bibliometrix (R), while thematic narrative synthesis was performed using NVivo 14. Results reveal a clear four-phase technological evolution: conventional methods (2008–2015), machine learning adoption (2016–2020), deep learning and IoT integration (2021–2023), and multimodal and large language model era (2024–2026). Technical impacts dominated the corpus (accuracy improvements of 18–35%), while social and economic impact studies remain critically underrepresented (2.6% and 0.9%, respectively). Key research gaps identified include poor model interpretability (black-box problem), limited integration with decision support systems (DSS), inadequate tropical-specific model development, and the near-total absence of longitudinal impact evaluations. This study contributes a holistic synthesis integrating technological evolution with multidimensional impact analysis, offering strategic recommendations for developing more adaptive, transparent, and equitable AI rainfall prediction systems aligned with SDG 2, SDG 13, and SDG 15

Performance Evaluation of the BERT Model in Sentiment Analysis of DANA Application User Reviews

2026-05-18T12:40:33+07:00

The rapid growth of digital wallets in Indonesia generates a large volume of user reviews on platforms such as the Google Play Store that cannot be efficiently analyzed manually. This study aims to evaluate the performance of the BERT (Bidirectional Encoder Representations from Transformers) model in sentiment classification tasks on a dataset of DANA application user reviews collected from the Google Play Store. The BERT model is fine-tuned using labeled Indonesian-language data with three sentiment classes: positive, negative, and neutral. Specialized preprocessing strategies are applied to handle the characteristics of informal text, abbreviations, and code-switching phenomena prevalent in Indonesian user reviews. Evaluation is conducted using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the fine-tuned IndoBERT model achieves an accuracy of 91.24% with a weighted F1-score of 0.91 on a test dataset of 6,106 samples. The Negative class achieves the highest performance with an F1-score of 0.95, followed by the Positive class (0.88) and Neutral class (0.84). This study provides empirical evidence of the effectiveness of the IndoBERT Transformer architecture for sentiment analysis in the Indonesian-language fintech domain and can serve as a reference for developing deep learning-based NLP systems in similar contexts.

Design of a Web-Based Village Tourism Management Information System with Multi-Tenant Architecture as an Integrated Platform: The Nusa Wisata Module

2026-05-18T18:08:40+07:00

The rapid development of information technology has significantly impacted various sectors, including tourism. However, in practice, village tourism management is still commonly conducted conventionally and lacks integration, resulting in various issues related to service efficiency, data management, and operational transparency. This study aims to design a web-based village tourism management information system using a multi-tenant architecture approach in the Nusa Wisata module as part of the integrated Nusa Eka platform.The research method employed in this study is system design using the System Development Life Cycle (SDLC) approach, focusing on the requirements analysis and system design stages. System modeling was conducted using Entity Relationship Diagram (ERD) for database design and flowcharts to illustrate the system process flow. The results of this study are in the form of a system design capable of integrating the management of multiple villages within a single platform while maintaining data separation through the use of the tenant_id attribute. The system is designed with several main features, including e-ticketing, tourism package and attraction management, tourism operational staff management, and parking management with an automated revenue-sharing mechanism. In addition, the system supports integration with other modules within the Nusa Eka platform, such as Nusa Praja, Nusa Graha, and Nusa Artha. Based on the design and analysis results, the proposed system provides a more integrated, flexible, and scalable solution compared to previous systems that still use a single-tenant approach. This system design is expected to improve operational efficiency, data transparency, and service quality in digital-based village tourism management.

Web-Based Congregation Data Management Information System for the Pamalar Sumba Christian Church

2026-05-19T14:21:52+07:00

Information technology plays an important role in improving efficiency, accuracy, and accessibility in administrative processes. Churches with large and dispersed congregations often face difficulties in managing and searching congregation data. The Sumba Christian Church, located in Umbu Langang Village, Central Sumba Regency, also experiences these challenges. The congregation consists of 1,231 members, including 589 males and 642 females, and the number continues to grow. Currently, congregation data is still recorded manually in books, making data updates slow, inefficient, and prone to errors or data loss due to non-centralized storage. To address these problems, this study developed a web-based Congregation Data Management Information System using the Waterfall development method. The system aims to simplify data recording, updating, and searching processes, making church administration more effective and efficient. The implementation of this system is expected to improve the speed, accuracy, and reliability of congregation data management. System testing was conducted using the Black Box Testing method, which showed that all system features functioned successfully with a 100% success rate. In addition, usability evaluation using the System Usability Scale (SUS) produced an average score of 80.75, indicating that the system is highly usable and acceptable for church administration activities.

Design and Development of a News Website and CMS Based on Three-Tier Architecture: A Case Study of PLTU Pangkalan Susu

2026-05-23T21:04:59+07:00

Pangkalan Susu Steam Power Plant does not yet have a structured official publication platform, causing information dissemination to depend mainly on social media and making news archives difficult to organize and retrieve regularly. This condition highlights the need for a web-based information system that supports more professional content management. This study aims to design and develop a news website and Content Management System (CMS) by applying a Research and Development (R&D) approach and three-tier architecture, which separates the presentation layer, application logic layer, and data layer. The development process consists of requirements analysis, system architecture and database design, implementation, and functional testing. The system was implemented using Laravel as the application framework, Blade for the user interface, Filament as the administration panel, and MySQL as the database management system. Functional testing was conducted using the black-box method on 14 main scenarios, including the management of news, categories, authors, banners, and logout features. The results show a 100% success rate, indicating that all tested functions operated according to the expected outcomes without functional errors. The proposed architecture produces a modular, scalable, and maintainable system that can support the future development of the official information platform of Pangkalan Susu Steam Power Plant.

Designing the Foundation of a Multi-Tenant and Surrogate Key-Based Nusa Praja Village Government System on the Nusa Eka Platform

2026-05-23T21:21:11+07:00

Village government administration in Indonesia is still largely manual, resulting in service inefficiencies and the potential loss of sensitive population data. The purpose of this research is to create the architectural foundation of a village government information system called NUSA PRAJA, which is part of the NUSAEKA multi-tenant Software as a Service (SaaS) platform. This research applies the Multi-Tenant Isolation concept to ensure data security and separation between villages, as well as the Surrogate Key concept to protect residents' National Identification Number (NIK) data from leaks. The research method used is Waterfall with the stages of requirements analysis, system architecture design, and database design. The results of the research are a multi-tenant system architecture design based on a shared database with tenant_id filters, an Entity Relationship Diagram (ERD) design with 14 main tables, and a flowchart design for three user roles. This project is expected to be a strong foundation for building a secure, scalable, and easy-to-use digital village system for village governments throughout Indonesia.

Designing a Key Performance Indicator Application for Sales Performance Evaluation Using the Web-Based ROC Method (Case Study of PT. Valve Automation Indonesia)

2026-05-25T13:21:04+07:00

Sales performance assessment is an important aspect in human resource management because it directly influences the achievement of company targets. At PT. Valve Automation Indonesia, KPI weighting is determined by managers based on personal assessments, this causes weight imbalances when adding or reducing assessment parameters. This study aims to design and implement a web-based Key Performance Indicator (KPI) application with the Rank Order Centroid (ROC) method to determine the weight of criteria based on the order of importance so as to produce a more objective and proportional weighting. The assessment criteria used include Absence, Sales Revenue, New Customers, Payment Collection, Visit Customers, and Follow Up Progress. This application was developed through the stages of needs analysis, design, implementation, and testing. This application shows that it is able to calculate KPI values automatically, generate sales performance rankings, and assist managers in making decisions more effectively and efficiently.

Network Device Performance Monitoring Using the Simple Network Management Protocol (SNMP) Method

2026-05-16T23:33:23+07:00

Network problems frequently occur at Politeknik Negeri Pontianak due to the increasing number and scale of network devices. These issues require continuous monitoring to ensure service availability across all network devices. To address this problem, the author conducted network monitoring using the SNMP (Simple Network Management Protocol) method and network performance measurement using the Wireshark application. SNMP is a standard protocol used to monitor and manage network devices such as routers, switches, servers, and other networking equipment. The research stages began with data collection, followed by monitoring and performance testing of the network. After testing the network in the Informatics Engineering Building, both satisfactory and unsatisfactory results were obtained. The results of SNMP measurements on MRTG showed the lowest throughput values on the second day of testing, with 485.6 kbps for daily traffic, 236.8 kbps for weekly traffic, 232 kbps for monthly traffic, and 121.6 kbps for yearly traffic. Meanwhile, the Quality of Service measurement produced the lowest throughput value of 0.225 kbps, packet loss of 0.354%, delay of 3.331 ms, and jitter of 8.763 ms.

Public Sentiment Analysis on the Issuance of Panda Bonds as an Effort for Rupiah Stability using SVM Algorithm on Youtube Social Media

2026-05-16T23:35:59+07:00

The stability of the Rupiah exchange rate is a crucial indicator of Indonesia's economic health, one of which is pursued through the issuance of Panda Bonds. However, this policy has triggered dynamic discourse on social media, particularly YouTube. This study aims to map public perception and test the performance of the Support Vector Machine (SVM) algorithm in classifying sentiments related to this issue. The research methodology includes scraping YouTube comment data, text preprocessing, automated labeling using the Lexicon-based method, and classification using SVM with a Linear kernel. From a total of 659 collected data, the results show that public sentiment is dominated by positive responses at 51.9%, followed by neutral sentiment at 29.0%, and negative sentiment at 19.1%. While public concerns focus on the debt burden and foreign currency dependence, there is overall support for economic stability efforts. The model evaluation demonstrates excellent performance, achieving an accuracy rate of 87.86%, precision of 88.79%, and an F1-score of 87.96%. This proves that a hybrid approach between Lexicon-based and SVM is effective in analyzing complex public opinions within the economic domain on social media.

Design of a Multi-Tenant SaaS-Based Centralized Financial System Using a Silent Accounting Approach

2026-05-18T12:39:11+07:00

Village financial management faces various fundamental challenges, including transaction recording that is still manual, a lack of integration between financial systems and village operational activities, and the absence of a platform capable of serving multiple villages within a single efficient infrastructure. These conditions result in financial reporting processes that are inefficient, error-prone, and difficult to account for. This study aims to design a centralized financial system based on a multi-tenant Software as a Service (SaaS) architecture using the Silent Accounting approach, defined as an automated transaction recording mechanism triggered by operational module activities without manual intervention. This study employs a qualitative descriptive method with a literature review approach. The design yields three main artifacts a system flowchart illustrating the workflow from user authentication, role assignment, and transaction validation through to automatic journal entry and posting to general ledger an Entity Relationship Diagram (ERD) modeling the database structure consisting of seven entities and a Data Flow Diagram (DFD) breaking down the system into five main processes A multi-tenant architecture with a ‘tenant_id’ column ensures data isolation between villages while allowing a single platform to serve multiple village simultaneously. The Silent Accounting mechanism ensures that all village financial activities are recorded consistently, accurately and in real time. The design is expected to serve as the foundation for the development and scalable village financial management platform.

Analysis of Green Computing Implementation in Efforts to Improve Resource Efficiency in the Campus Environment

2026-05-18T12:49:15+07:00

This study aims to analyze the level of green computing implementation in efforts to improve resource efficiency in the campus environment. The primary problem addressed is the high energy consumption in higher education environments due to the increasing use of information technology devices, necessitating efficient and sustainable energy management measures. This study employs a mixed methods approach, combining literature review with quantitative data collection through questionnaires to obtain data on user understanding and behavior regarding green computing. The results indicate that the majority of respondents demonstrate a good understanding of energy efficiency. Based on the data obtained, the authors conclude that the level of awareness and implementation of green computing among students is very good. The findings also reveal that students have a high concern for the impact of energy consumption on the environment and support energy conservation efforts. Overall, this study demonstrates that the application of green computing has great potential for development through broader research and targeted campus policies. This research is expected to serve as a foundation for developing more efficient energy policies in higher education.

Performance Evaluation of Machine Learning Algorithms in Sentiment Analysis of Spotify Reviews

2026-05-18T18:12:18+07:00

The rapid growth of digital music streaming platforms has generated a massive volume of user reviews on the Google Play Store, making manual analysis practically infeasible. This study evaluates and compares the performance of three machine learning algorithms Support Vector Machine (SVM), Neural Network (Multilayer Perceptron), and Random Forest in classifying sentiments from Spotify user reviews written in Indonesian. A total of 10,000 reviews were collected from the Google Play Store using the google-play-scraper library and processed through a text preprocessing pipeline comprising cleaning, case folding, word normalization, tokenization, stopword removal, and stemming using the Sastrawi library. Sentiment labeling was performed automatically using the InSet lexicon, categorizing reviews into three classes: Positive (56.63%), Neutral (30.60%), and Negative (12.76%). Feature extraction was conducted using the TF-IDF method, with an 80:20 train-test split strategy and stratified sampling to maintain class distribution. Model performance was evaluated based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that SVM and Neural Network achieved equivalent and superior accuracy of 0.937, with macro F1-scores of 0.908 and 0.907, respectively, outperforming Random Forest which recorded an accuracy of 0.853 and a macro F1-score of 0.777. These findings indicate that SVM and Neural Network are more optimal and reliable for sentiment classification of Indonesian-language Spotify reviews, while Random Forest requires further improvement, particularly in recognizing minority classes.

Topic Modeling of Clash of Clans Player Reviews Using NLP-Based Latent Dirichlet Allocation (LDA) Machine Learning Method

2026-05-19T14:17:53+07:00

The rapid growth of the mobile gaming industry has generated millions of player reviews on platforms like the Google Play Store. Clash of Clans, developed by Supercell, is one of the world's most popular mobile strategy games, generating a vast volume of user reviews that are difficult to analyze manually. This study applies Latent Dirichlet Allocation (LDA), a generative probabilistic machine learning model based on Natural Language Processing (NLP), to identify and cluster key topics discussed in player reviews on the Google Play Store. A total of 10,000 player reviews were collected through web scraping, followed by NLP-based text preprocessing including tokenization, stopword removal, and lemmatization. The LDA model was optimized using a coherence score evaluation of 0.512, resulting in the identification of five dominant discussion topics: technical issues and bugs, game updates and balance, gameplay and strategy, monetization and in-app purchases, and social interactions and clan systems. The results show that LDA-based topic modeling provides structured and actionable insights for game developers to understand player feedback and improve game quality. This research contributes to the field of NLP-based mobile game review analysis.

Integration of the Webqual 4.0 and UEQ Methods in Evaluating the Impact of the Student Recruitment Information System on Business Efficiency and Admissions Services (Case Study: Sari Mutiara Indonesia University)

2026-05-23T21:08:35+07:00

This study aims to evaluate the service quality of the New Student Admission Information System (PMB) at Sari Mutiara Indonesia University and its impact on business efficiency using the WebQual 4.0 method and the User Experience Questionnaire (UEQ). Based on a population of 1,700 users, the sample size was determined using the Slovin formula (5% margin of error), resulting in 324 respondents (319 prospective students, 5 admissions staff) selected via purposive sampling. The WebQual 4.0 evaluation yielded an average score of 4.05 (Good category), with the Usability dimension scoring highest (4.30) and Service Interaction lowest (3.80). UEQ analysis showed that all scales fell within the positive category (>0.8), with the main strength lying in the pragmatic aspect of Perspicuity (2.36), whilst recording the lowest score in the hedonic aspect of Novelty (1.47). Operationally, the system’s high ease of use has been shown to reduce data verification time by 75% (from 20 to 5 minutes) and lower the human error rate from 12% to below 2%. In conclusion, the PMB system operates very efficiently from a business perspective and is user-friendly; however, an update to the user interface (UI/UX) is urgently needed to enhance the novelty value, along with the integration of a more responsive technical support service.

Implementation of the K-Means Method for Developing an Air Quality Monitoring Information

2026-05-23T21:16:17+07:00

Air quality in Bogor City is becoming increasingly complex due to the rising number of motor vehicles, small-scale industrial activities, and seasonal dynamics that are difficult to analyze using conventional methods. The Environmental Agency of Bogor City routinely monitors air quality through the Air Quality Monitoring System (AQMS); however, data utilization remains confined to monitoring and reporting, necessitating advanced analysis to achieve a more comprehensive view of air quality patterns. This study aims to classify time periods based on air quality parameters using the K-Means clustering method to identify good and bad air pollution categories. The research data was obtained from the Bogor City Environmental Agency's AQMS for the period from January 2023 to June 2025. The results indicate that the K-Means method successfully clustered the data into two groups: good and bad air quality categories. Good air quality was identified in 2023 (January, February, March, April, November, and December), 2024 (January, February, March, April, October, November, and December), and 2025 (January to May). Conversely, poor air quality occurred in 2023 (May to October), 2024 (May to September), and 2025 (June). The findings of this research are expected to support pollution control strategies and early warning systems based on air quality data.

Analysis of Trends and Development of Low-Light Image Enhancement Methods in Computer Vision

2026-05-23T21:28:31+07:00

This study investigates the development of Low-Light Image Enhancement (LLIE) methods in the field of computer vision using a Systematic Literature Review (SLR) approach. The review was conducted on 56 scientific articles selected from a total of 604 papers entirely sourced from the Scopus database based on the PRISMA 2020 guidelines. The results indicate that LLIE research has evolved from traditional methods, such as histogram equalization and Retinex, toward deep learning-based approaches including CNN, GAN, Transformer, and diffusion models. Modern methods have demonstrated superior performance in improving image illumination, preserving details, and reducing noise. In addition, real-world datasets and zero-reference approaches are increasingly adopted to improve model generalization capability. However, challenges remain regarding computational complexity, detail preservation, and model performance under extreme low-light conditions. This study concludes that future LLIE research will focus on developing models that are more adaptive, efficient, lightweight, and robust for various computer vision applications.

Analysis and Simulation of a Queueing System in a Self-Service Seblak MSME using the FIFO Model

2026-05-24T18:46:34+07:00

Micro, Small, and Medium Enterprises (MSME) Seblak Buffet often experience long queues and inefficient waiting times, especially during peak hours, which negatively impact customer comfort and service quality. This study aims to analyze and analyze the business's queue system using the FIFO (First In First Out) model to improve service efficiency and fairness. Using a quantitative approach with M/M/1 modeling, on arrival times and following exponential distribution service, data were obtained from direct observation. The simulation results show that although FIFO maintains order, waiting times increase significantly during peak hours due to limited facilities. Therefore, this study recommends adding waiters or rearranging service flows during peak hours as an applicable solution to improve quality and customer satisfaction.

Development and Evaluation of a Desktop Academic Data Encryption Application Using AES with Password-Based Key Management

2026-06-15T12:06:45+07:00

This study evaluates a desktop-based academic data security application integrating the Advanced Encryption Standard (AES) and Password-Based Key Derivation Function 2 (PBKDF2) at Pondok Pesantren Nur Rohman. Using an experimental approach, three AES key lengths (128-bit, 192-bit, and 256-bit) were tested against 10 authentic academic files to assess computational performance and cryptographic strength. The results show that AES-128 achieves the fastest speed with a throughput of 57.84–65.00 MB/s, while AES-256 requires the longest processing time due to its 14 internal rounds. Regarding security, all variations exhibit ideal mathematical resilience: ciphertext entropy ranges tightly between 7.9988 and 7.9999 (near absolute randomness), and avalanche effect percentages remain stable between 49.85% and 50.19%. Furthermore, PBKDF2 successfully mitigates brute-force vulnerabilities by mapping passwords into precise hexadecimal keys. The process introduces a minimal, constant file size overhead of exactly 64 bytes (Salt, IV, and padding). Hardware utilization is exceptionally efficient, recording low CPU usage (0.1%–0.2%) and stable RAM allocation (53%–54%). In conclusion, the system delivers an optimal equilibrium between high-level data protection and local hardware efficiency.

AI-Based Chatbot Development for Academic Information Services at Universitas Negeri Medan and an Analysis of Indonesian Language Usage

2026-05-25T13:18:47+07:00

The efficiency of academic information services remains a major challenge in higher education institutions, particularly in responding to student inquiries quickly and independently. This study develops an artificial intelligence-based chatbot system for academic information services at Universitas Negeri Medan (UNIMED) by utilizing the LLaMA 3.1 8B Instant Large Language Model (LLM) via the Groq API within a Retrieval-Augmented Generation (RAG) framework. The system was built using a three-tier architecture consisting of a React.js (Vite) frontend, a Node.js with Express backend, and a Supabase (PostgreSQL) database serving as the academic FAQ knowledge base. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was adopted as the research methodology. System evaluation was conducted using Black-Box Testing across four main scenarios: questions available in the FAQ, follow-up questions requiring conversational context, off-topic questions, and reference link validation all of which yielded a pass status. Furthermore, Indonesian language testing demonstrated that the system is capable of understanding diverse student language variations, including formal language, informal expressions, academic abbreviations, and ambiguous queries, while maintaining appropriate academic communication etiquette. The results indicate that the RAG approach is effective in reducing AI hallucination risks, and that this web-based chatbot offers broader accessibility compared to previous messaging platform-based chatbot systems.

A Content-Based Filtering Approach Using TF-IDF and Cosine Similarity for Hotel Recommendation Based on Traveloka Accommodation Data: A Case Study of Jakarta

2026-05-26T10:19:39+07:00

The development of Online Travel Agents (OTA) has generated a large and diverse volume of accommodation data, which often makes it difficult for users to select hotels that match their preferences in terms of location, facilities, price, and service reputation. This study is a continuation of a previous work on Traveloka accommodation data acquisition using web scraping based on the data-testid attribute. The focus of this research is to utilize the scraped data for developing a machine learning-based hotel recommendation system using a content-based filtering approach. The dataset used consists of 1,809 hotel records in the Jakarta area with attributes including hotel name, property type, star rating, rating score, location, price, facilities, and image URL. Data preprocessing includes price cleaning, separating rating scores and number of reviews, and combining location, facilities, and property type as textual content representation. The recommendation model is built using Term Frequency–Inverse Document Frequency (TF-IDF) to construct text feature vectors, followed by Cosine Similarity to measure similarity between hotels. In addition, this study introduces a weighted popularity score that combines rating values and the number of reviews to ensure that recommendations are not only based on content similarity but also reflect the credibility of hotel popularity. Experimental results produce a TF-IDF matrix of size 1,364 × 371 and a similarity matrix of 1,364 × 1,364. Functional testing shows that the system is capable of generating ten relevant hotel recommendations based on similarity in location, facilities, and property type, which are then ranked according to the popularity score.

Application of the Mobilenet Model for Pest Detection in Mustard Plants Based on Leaf Imagery

2026-05-27T15:04:06+07:00

Mustard greens (Brassica rapa) are one of the most widely cultivated vegetables in Indonesia due to their high economic value and nutritional content. However, the productivity of mustard plants often decreases because of pest attacks such as armyworms (Spodoptera litura) and diamondback moth caterpillars (Plutella xylostella). The process of pest identification that is still performed manually is considered inefficient and prone to human error. Therefore, a technology-based system is needed to automatically and accurately detect pests. This study aims to develop an Android-based pest detection application for mustard plants by implementing the MobileNet model based on leaf images. The method used in this research is Convolutional Neural Network (CNN) with the MobileNet architecture because it is lightweight and efficient for mobile devices. The dataset used consists of 1,380 mustard leaf images, including 1,241 training data and 139 testing data. The research stages include data collection, image preprocessing, MobileNet model training, and model evaluation using a confusion matrix. The results of this study show that the developed application is capable of detecting the condition of mustard leaves, whether healthy or infected by pests. The MobileNet model achieved a training accuracy of 97% and a validation accuracy of 95%–98%, indicating that the model can effectively recognize leaf damage patterns. In addition, the application was successfully implemented on Android devices with gallery, camera, cropping, and automatic detection features, making it easier for users to identify pests on mustard plants quickly and practically.

Early Warning System for Detecting Student Dropouts Using the Random Forest Algorithm at SMKS Alhuda

2026-05-28T09:23:06+07:00

The dropout rate in vocational high schools poses a serious challenge that requires an objective early-detection system. This study aims to optimize a model for predicting student dropout risk by utilizing a supervised learning approach. The study uses multivariate data covering student demographic attributes, academic achievement, behavior, and financial history. The Random Forest algorithm was implemented to classify student risk levels into Safe, Caution, and Danger categories to support preventive decision-making. Model performance testing using a confusion matrix showed an accuracy rate of 99%, with a recall of 100% in the High-Risk category, demonstrating the algorithm’s effectiveness in accurately identifying high-risk students. These findings contribute to the development of more precise early detection methods in educational settings.

E-Commerce Website Design with a Content Based Filtering Product Recommendation System at the Aqua Factory in Ruteng City

2026-05-30T17:10:06+07:00

This study focuses on the design and development of an e-commerce website for the AQUA Factory in Ruteng City by implementing a Content-Based Filtering recommendation system. The system is designed to help customers find products that better match their preferences and needs from a large number of available options. Content-Based Filtering was chosen because it can provide personalized recommendations based on product attributes viewed or selected by users without requiring interaction data from other users. Compared to Collaborative Filtering and Hybrid Filtering, this method is considered more efficient in the early stages of system development. The implementation of this recommendation system is expected to improve user experience, increase ordering efficiency, and support the product distribution process in the AQUA Factory in Ruteng City.

The Transformation of Banking Frontliner Competencies in the Digital Age: A Systematic Review

2026-06-06T20:17:03+07:00

The banking sector is currently at a crucial juncture in its digital transformation, demanding maximum operational efficiency through system automation. However, automation often creates a gap in the quality of emotional service that can only be filled by the human element. This study aims to construct a conceptual synthesis regarding strategies for adapting the competencies of banking frontline staff to maintain the relevance of their roles in the era of digitalisation. The method used is a Systematic Literature Review (SLR) of 18 reputable scientific articles published between 2021 and 2026. The thematic synthesis indicates that digitalisation does not automatically replace the human role, but rather demands a transformation of competencies from clerical transactional skills towards digital financial literacy, self-efficacy, and emotional intelligence in an advisory capacity. Critical findings reveal that a failure to upskill digital competencies contributes to high rates of human error due to cognitive fatigue. In conclusion, banking management must shift the training paradigm from mere technical tool mastery towards the development of human-centred adaptive capabilities. The managerial implications of this study emphasise the importance of institutionalising mentoring and personalised career development strategies to mitigate technological resistance.

Heart Disease Prediction Using a Comparison of Naïve Bayes and Random Forest Algorithms

2026-06-03T13:13:31+07:00

Heart disease is one of the leading causes of death worldwide, making early detection essential. This study compares the performance of the Naive Bayes and Random Forest algorithms in predicting heart disease using clinical data. The dataset includes attributes such as chest pain type (cp), maximum heart rate achieved (thalach), and slope of the ST segment (slope). The research process consists of data preprocessing, feature selection, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that Random Forest outperformed Naive Bayes in heart disease prediction. Random Forest achieved an accuracy of 75%, precision of 69%, and recall of 86%, while Naive Bayes achieved an accuracy of 69%, precision of 66%, and recall of 72%. These findings indicate that Random Forest is more effective in handling the complexity of heart disease data and provides better predictive performance. This study demonstrates the potential of machine learning methods, particularly Random Forest, in supporting heart disease diagnosis and may serve as a reference for the development of medical decision support systems.

Performance of Yolov8 Algorithm and Real-Time Detection Transformer in Tomato Ripeness Detection System

2026-06-06T21:06:46+07:00

Tomato ripeness sorting is still widely carried out manually and subjectively, which can lead to inconsistencies in the quality of the sorting results. In addition, the manual process requires more time and has the potential to cause errors in classifying tomato ripeness levels. Therefore, an automatic detection system based on digital images is needed to provide more accurate and consistent detection results. This study aims to analyze and compare the performance of the You Only Look Once (YOLOv8) and Real-Time Detection Transformer (RT-DETR) algorithms in detecting and classifying tomato ripeness levels based on digital images. The research method used is an experimental method consisting of dataset collection, data labeling, image augmentation, data splitting into training, validation, and testing sets, as well as model training using Google Colab. The tomato ripeness levels were classified into six classes to provide a more detailed representation compared to previous studies. Model performance evaluation was carried out using accuracy, precision, and recall metrics. The results showed that YOLOv8 achieved a precision value of 64.8%, recall of 70%, and accuracy of 50.7%. Meanwhile, RT-DETR demonstrated better performance with a precision of 80.8%, recall of 84%, and accuracy of 70%. Based on these results, RT-DETR is considered superior in providing more accurate and consistent predictions, making it more potential to be implemented in a digital image-based tomato sorting system to improve the efficiency and quality of agricultural products.

The Role of Generative AI in the Software Development Life Cycle: A Systematic Literature Review

2026-06-06T21:21:06+07:00

The rapid advancement of Generative Artificial Intelligence (Generative AI), particularly through the emergence of Large Language Models (LLMs), has significantly transformed modern software engineering practices. These technologies enable automation across various phases of the Software Development Life Cycle (SDLC), including system design, coding, testing, and software maintenance. Despite their potential to improve development efficiency, the widespread adoption of Generative AI also introduces critical concerns related to software security, code quality, and long-term maintainability. This study aims to analyze the opportunities, security risks, and mitigation strategies associated with the integration of Generative AI into the SDLC. A Systematic Literature Review (SLR) with a qualitative descriptive approach was conducted by examining 15 primary studies published between 2021 and 2026, retrieved from IEEE Xplore, ACM Digital Library, Scopus, Google Scholar, and Portal Garuda. The collected literature was analyzed using content analysis and thematic analysis to identify the impacts of Generative AI across different SDLC phases. The findings reveal that Generative AI significantly enhances developer productivity, achieving efficiency gains of approximately 35% during system design, 55% during coding, 45% during testing, and 40% during software maintenance. However, AI-generated code remains vulnerable to various security weaknesses, including SQL Injection, Cross-Site Scripting (XSS), and improper input validation. Furthermore, excessive reliance on AI-generated outputs may contribute to technical debt accumulation through code duplication and reduced refactoring activities. To address these challenges, this study recommends the implementation of a Stratified AI-Human Governance Framework (SAHGF), which combines automated security validation, human code review, security testing, and continuous monitoring.

Implementation of a Responsive Web Interface for IoT-Based Scoliosis Monitoring Visualization

2026-06-07T15:52:44+07:00

The rapid advancement of digital health technology has increased the demand for real-time and continuous patient monitoring systems, particularly for conditions such as scoliosis that require routine postural assessment. Conventional monitoring approaches often lack effective data visualization and multi-device accessibility, limiting their practical use in healthcare environments. This study aims to design and implement a responsive web interface for real-time visualization of data generated by an Internet of Things (IoT)-based scoliosis monitoring system. The Research and Development (R&D) approach was applied through four stages: system requirement identification, interface design, system implementation, and system evaluation. Data were collected from IoT sensors measuring the Angle of Trunk Rotation (ATR) and transmitted continuously to a web-based dashboard. Evaluation results demonstrated that the system successfully performed stable real-time data transmission, maintained proper synchronization between the IoT device and the server, and displayed monitoring information through responsive graphical and indicator-based visualizations on both desktop and mobile devices. The proposed interface improves data accessibility, supports early detection of scoliosis, and facilitates faster interpretation of patient condition data, contributing to more effective digital healthcare monitoring.

Analysis and Design of a Decision Support System in Employee Selection using the AHP (Analytical Hierarchy Process) Method

2026-06-08T12:06:41+07:00

Employee performance assessment is a crucial aspect in determining the quality of human resources that contribute to a company's success. However, the process of selecting the best employees at PT. Asia Carton Lestari has been carried out manually, resulting in problems such as assessment inaccuracy, the lack of clear criteria weighting, and a high potential for human error. This study aims to design and build a web-based Decision Support System (DSS) using the Analytical Hierarchy Process (AHP) method to improve the effectiveness and objectivity of employee assessments. The AHP method was chosen because of its ability to solve multi-criteria decision-making problems through pairwise comparisons and systematic calculation of priority weights. Four assessment criteria are used in this system: attendance, work quality, discipline, and responsibility. Implementation results indicate that the system is capable of generating recommendations for selecting the best employees more accurately and consistently, with the highest score of 0.229593 obtained by an employee named Adam as the best alternative. Furthermore, black box testing results indicate that all system functions are functioning as expected. Thus, this AHP-based DSS can be an effective solution for companies in improving the quality of employee performance assessments.

Design of a Web-Based Decision Support System for Laptop Vendor Search for Resellers using the Simple Addtive Weighting (SAW) Method

2026-06-08T14:13:27+07:00

This study aims to design a web-based Decision Support System (DSS) to assist resellers in determining the most suitable used laptop vendors to meet their criteria at the Loyal Laptop store. The main problem faced is the vendor selection process and stock management, which are still done manually, often resulting in data inaccuracies, recording errors, and delays in obtaining important information regarding stock and vendor quality. To overcome this, this study developed a system using the Simple Additive Weighting (SAW) method as a calculation technique in determining the best vendor based on several criteria, namely delivery speed, discount level, service, warranty, product authenticity, and payment terms. Each criterion is given a weight according to its level of importance in the vendor selection process, then a normalization and ranking process is carried out to obtain the best alternative. The system is built web-based using PHP, HTML, CSS, and MySQL as a database. The implementation results show that the system can help admins in managing vendor data more effectively, improve the accuracy of vendor selection, and speed up the decision-making process. With this application, the process of processing stock data and searching for vendors becomes more efficient, precise, and structured.

Expert System for Diagnosing Feline Infectious Peritonitis (FIP) Viral Infection in Cat using the Certainty Factor Method

2026-06-08T17:28:05+07:00

Feline Infectious Peritonitis (FIP) is one of the most dangerous viral infectious diseases in cats that can lead to death if not treated quickly and properly. This disease is difficult to detect at an early stage because it has symptoms similar to those of other diseases, causing many cat owners to experience difficulties in identifying their pets’ health conditions. The lack of knowledge regarding the early symptoms of FIP, along with the limited number of veterinarians and the relatively high cost of medical examinations, has become an obstacle in the process of diagnosing diseases in cats. Therefore, an expert system is needed to assist in the early diagnosis process of Feline Infectious Peritonitis (FIP) based on the symptoms experienced by cats using the Certainty Factor method. Based on the analysis and Certainty Factor calculations conducted according to the selected symptoms, the diagnosis result obtained was Dry Feline Infectious Peritonitis (FIP) with a confidence level of 97.45%.

Implementation of Naïve Bayes Algorithm for Sentiment Classification of Public Youtube Opinions Related to Nickel Mining Issues in Raja Ampat

2026-06-06T20:37:10+07:00

Indonesia currently holds the world’s largest nickel reserves. However, extractive activities in the Raja Ampat conservation area pose significant ecological threats and trigger social polarization on social media. The massive volume of opinion data creates challenges for policymakers in mapping public perception quickly and objectively. This study aims to classify public sentiment regarding nickel mining activities in Raja Ampat using the Naïve Bayes algorithm with TF-IDF feature weighting. The methodology employed in this research is CRISP-DM, which consists of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment stages. The dataset consists of 10,903 YouTube comments collected from the Ferry Irwandi and Kompas.com channels. The results indicate that negative sentiment dominates public discourse at 46.4%, followed by neutral sentiment at 31%, and positive sentiment at 22.6%. The classification model achieved an accuracy rate of 71.59%. Furthermore, a Streamlit-based visualization dashboard was developed to assist stakeholders in monitoring public opinion trends systematically.

Load Balancing Optimization in Cloud Task Scheduling Using Genetic Algorithm

2026-05-26T12:49:25+07:00

Cloud computing environments face significant challenges in task scheduling and load balancing due to the increasing scale and complexity of computing workloads. Inefficient task scheduling leads to uneven resource utilization, increased makespan, and higher operational costs. This research proposes load balancing optimization in cloud task scheduling using a Genetic Algorithm applied to the Cloud Task Scheduling Dataset. The dataset underwent preprocessing including categorical encoding, data cleaning, and Min-Max Normalization prior to the optimization process. The Genetic Algorithm was implemented using Tournament Selection, Two-Point Crossover, and Uniform Integer Mutation, with the fitness function formulated based on makespan and degree of load imbalance minimization. The performance of the proposed approach was evaluated against a Random Assignment baseline across five metrics: makespan, degree of load imbalance, load distribution efficiency, average per-task completion time, and computational cost. The results demonstrated that the Genetic Algorithm significantly outperformed, achieving a makespan reduction of 51.33%, a load imbalance reduction of 43.81%, a load distribution efficiency improvement of 11.24%, an average per-task completion time reduction of 26.37%, and a computational cost reduction of 19.70%. These findings confirm that the Genetic Algorithm is an effective approach for optimizing task scheduling and load balancing in cloud computing environments.

Design of Foster Children Data Information System at Rumah Harapan Karawang Foundation

2026-05-29T15:47:51+07:00

The Rumah Harapan Foundation is a social organization dedicated to supporting foster children from diverse backgrounds through education, guidance, and sustainable empowerment programs. Currently, the management of foster child data is conducted manually and centralized within the dormitory division, leading to various inefficiencies such as delayed information access, slow program execution, and reduced service quality for both children and donors. To overcome these challenges, this study designs a web-based information system that enables structured, integrated, and real-time data management according to user access levels. The research applies the Design Thinking methodology, consisting of the stages Empathize, Define, Ideate, Prototype, and Test. Data were obtained through interviews, observations, and literature studies to identify user needs and system requirements. The resulting prototype features core functions such as data entry for foster children and dormitories, search, editing, report generation, and role-based access control for administrators, directors, and users. Based on interface testing, the system operates effectively and aligns with user expectations. It is expected to enhance operational efficiency, accelerate decision-making, and strengthen interdepartmental coordination within the Rumah Harapan Foundation.

Analysis of the Implementation of the ITIL Framework in Information Technology Service Management at Universities

2026-05-30T08:26:44+07:00

This study examines the application of the Information Technology Infrastructure Library (ITIL) framework in information technology service management at universities. The method used was a literature review employing a qualitative descriptive approach to 15 scientific articles obtained from various academic databases. The results show that the Service Operation domain is the most widely applied, particularly in the incident management and service desk processes. The application of ITIL has proven to help improve service quality and the speed of incident resolution. However, ITIL implementation remains limited to operational aspects and does not yet cover the entire ITIL lifecycle comprehensively. The main challenges identified are limited human resources and a lack of understanding of the ITIL framework.

Implementation of the Mamdani Fuzzy System as a Decision Support for Contract Extensions for Non-Staff Employees in Manufacturing Companies

2026-05-30T17:15:33+07:00

This study applies the Mamdani Fuzzy method as a decision support system in determining the feasibility of contract extension for non-staff employees in a manufacturing company. Performance assessment is conducted based on seven criteria, namely Attendance (C1), Work Quality (C2), Discipline (C3), Teamwork (C4), Initiative (C5), Responsibility (C6), and Professional Attitude (C7). The assessment uses the company's official linguistic scale: Very Poor (1), Poor (2), Sufficient (3), Good (4), and Very Good (5). Based on company regulations, employees with a total performance score below 23 are declared to have their contracts discontinued (termination), while employees with a score ≥ 23 are entitled to a contract extension or be appointed as permanent employees. The dataset consists of 40 non-staff employees from ten departments. The fuzzy inference process includes fuzzification, rule base formation, min-max inference, and centroid defuzzification. The results showed that 11 employees (27.5%) were recommended to have their contracts terminated, 16 employees (40.0%) were recommended to have their contracts extended, and 13 employees (32.5%) were recommended to be appointed as permanent employees. This system has proven to be able to produce recommendations that are objective, transparent, and in line with official company regulations.

Design of a Web-Based IT Service System Using ITIL at Dunggala Village

2026-06-01T08:11:42+07:00

This community service activity was conducted because the administrative service process at the Dunggala Village Office is still carried out manually, often causing delays and inefficient data management. This demonstrates the importance of implementing a technology-based system to improve the quality of public services at the village level. The method used is a qualitative approach through observation, interviews, and documentation. The system was designed using the Waterfall model and applying ITSM concepts with the ITIL framework. The results show that the web-based SIPEDES system (Sistem Informasi dan Pelayanan Administrasi Desa) can accelerate the service process, make it easier for the community to access services through the citizen complaint feature and online letter service, and assist village officials in managing data in a more structured manner. Based on evaluation using the System Usability Scale (SUS) method with 7 respondents, the system received ratings in the good to very good category.

Gamer Behavior Modeling Using Computer Vision and Artificial Intelligence

2026-06-04T12:35:13+07:00

This systematic literature review examined game-player behavior modeling that integrates computer vision and artificial intelligence. A total of 50 scientific articles from the Scopus database, published between 2017 and 2025, were analyzed using the PRISMA 2020 protocol to address five research questions. The results showed that eye-tracking metrics reliably indicated cognitive load and player attention in real time. Deep learning models could predict individual-specific behavioral styles in strategic games, and data-driven analysis effectively mapped players' spatial movement dynamics in large-scale game environments. Furthermore, physiological metrics in Virtual Reality proved effective as inputs to a dynamic difficulty adjustment system, and data-driven gaze-direction modeling significantly enhanced the realism of player interactions with virtual agents. These findings confirmed that the synergy between computer vision and artificial intelligence is a crucial foundation for creating adaptive and immersive gaming experiences.

Design and Implementation of Software and Angle Calibration System for an ESP32-Based Portable Digital Scoliometer

2026-06-04T21:15:48+07:00

Scoliosis is a spinal deformity characterized by an abnormal lateral curvature of the spine and requires accurate early detection to prevent further complications. Conventional scoliometers are still widely used for initial screening; however, manual measurements often face limitations in reading precision and data recording efficiency. This study aims to design software and an angle calibration system for a portable digital scoliometer based on the ESP32 microcontroller. The developed system utilizes an MPU6050 sensor to detect trunk rotation angle, an LCD module as a display interface, and wireless communication for data transmission. The software implementation includes sensor data acquisition, angle processing using the Kalman Filter method, automatic zero-point calibration, battery monitoring, and communication with a web-based server through a RESTful API. The calibration system is designed to reduce initial offset errors and improve measurement stability. Testing results indicate that the system is capable of displaying angle measurements in real time, operating responsively, and transmitting data successfully to the server. The implemented calibration method also improves consistency and reliability of angle readings. Therefore, the developed portable digital scoliometer can serve as an effective solution for supporting scoliosis screening in a more practical and digitalized manner

Design and Development of a Web-Based Tourism Information System (Case Study: Kanatang District)

2026-06-06T20:53:34+07:00

In the modern era, advances in information technology have significantly influenced many aspects of life, including the delivery of information through digital systems. One sector that can benefit from this development is tourism, particularly in Kanatang District, East Sumba Regency. Kanatang District has various tourism potentials, including natural attractions, historical sites, cultural heritage, and traditional customs that attract visitors. However, these potentials are not yet supported by an adequate information system to promote and provide information about tourist destinations. As a result, tourists often experience difficulties in obtaining complete, fast, and accurate information regarding tourism objects in the area. In addition, data from related agencies show a decline in tourist visits in 2023, indicating limited tourism promotion and poor accessibility of tourism information in the region. To overcome these problems, this study proposes the design and development of a web-based Tourism Information System that provides information about tourist attractions, interactive maps, and supporting tourism facilities in Kanatang District. The system is developed using the Waterfall method. The purpose of this research is to provide comprehensive tourism information and make it easier for tourists to access the location and description of each tourist destination in Kanatang District.

Sentiment Analysis on the Failure of the Indonesian National Team to the 2026 World Cup During Patrick Kluivert's Coaching Period using the Support Vector Machine (SVM) Algorithm

2026-05-17T09:13:59+07:00

This study aims to analyze public sentiment regarding the failure of the Indonesian National Team to qualify for the 2026 FIFA World Cup during Patrick Kluivert’s coaching period using the Support Vector Machine (SVM) algorithm. Data were collected through web scraping from Twitter (X), YouTube, and Detik.com, resulting in 5,060 comments. The collected data were processed using Natural Language Processing (NLP), including case folding, cleaning, tokenization, stopword removal, normalization, and stemming. The labeled data were transformed using the Term Frequency–Inverse Document Frequency (TF-IDF) method and divided into training and testing sets with an 80:20 ratio. The classification model was developed using a linear kernel SVM and implemented through a Streamlit-based web application for interactive sentiment prediction. The results showed that negative sentiment dominated with 55.0%, followed by positive sentiment at 36.4% and neutral sentiment at 8.6%. Model evaluation achieved an accuracy of 78.44%, precision of 78.54%, recall of 78.44%, and f1-score of 78.48%. These findings indicate that the SVM method is effective in classifying public sentiment toward the performance of the Indonesian National Team.

Design and Development of IoT-Based Smart Street Lighting with Lighting Level Adjustment Based on Environmental Sensors

2026-06-06T21:16:51+07:00

This research discusses the design of an Internet of Things (IoT)-based Smart Street Lighting system that is able to adjust lighting levels automatically based on environmental conditions. The system is implemented on a garden lamp prototype by utilizing Light Dependent Resistor (LDR) and Passive Infrared (PIR) sensors as the main input in regulating lamp intensity. The ESP32 microcontroller is used as a control center as well as an IoT communication module for monitoring and controlling the system through a real-time web interface. The research method used is prototyping which includes hardware design, software development, implementation, and system testing. The test results show that the system is able to regulate lighting automatically, namely the lights turn off in bright conditions, turn on dimly when dark conditions with no activity, and turn on brightly when activity is detected. In addition, the web-based monitoring system successfully displays the status of the lights and sensors in real-time. The developed system can be a more efficient, adaptive outdoor lighting solution and supports the application of IoT technology in smart lighting systems.

Design of a Web-Based Household Worship Scheduling Information System at GBI Wangga

2026-06-08T11:41:15+07:00

The rapid development of information technology has increased the need for effective information management in various organizations, including churches. At GBI Wangga, the scheduling of Household Fellowship Worship (PA) is still conducted manually through written records and verbal announcements, resulting in delays and uneven distribution of information among congregation members. This study aims to design and develop a web-based Household Fellowship Worship Scheduling Information System to facilitate schedule management and improve access to information. The system was developed using the Waterfall method, which includes requirements analysis, system design, implementation, and testing. The resulting system enables users to access worship schedules quickly and accurately through a web platform. It is expected to improve scheduling efficiency, reduce information delivery errors, and support better church services for the congregation.

Expert System for Diagnosing Gingivitis using the Certainty Factor Method

2026-06-08T14:09:17+07:00

Gingivitis is one of the oral health disorders caused by inflammation of the gum tissue. This disease is often considered mild, but if it is not treated promptly, it can develop into a more serious condition. The lack of public knowledge regarding the early symptoms of gingivitis, along with limited access to dentists and the relatively high cost of dental examinations, causes many sufferers to be late in recognizing their oral health condition. Therefore, an expert system is needed to assist in the early diagnosis of gingivitis quickly and easily using the Certainty Factor method. Based on the analysis and Certainty Factor calculations conducted according to the selected symptoms, the diagnosis result obtained was Acute Gingivitis with a confidence level of 98.98%.

Multiclass Classification of Retinal Diseases from OCT Images Using ResNet50-Based Transfer Learning

2026-06-09T09:29:55+07:00

Retinal diseases are among the leading causes of visual impairment and blindness when not detected at an early stage. Advances in artificial intelligence, particularly deep learning, provide opportunities to support automated retinal disease diagnosis using Optical Coherence Tomography (OCT) images. This study aims to develop a multiclass retinal disease classification model using ResNet50-based transfer learning on the Retinal OCT C8 dataset, which consists of eight retinal condition categories: Age-related Macular Degeneration (AMD), Choroidal Neovascularization (CNV), Central Serous Retinopathy (CSR), Diabetic Macular Edema (DME), Diabetic Retinopathy (DR), Drusen, Macular Hole (MH), and Normal. The research stages included data preprocessing, image augmentation, transfer learning-based model training, and fine-tuning of the final network layers. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrated that the proposed ResNet50 model achieved an accuracy of 93%, precision of 93%, recall of 93%, and F1-score of 93% on the testing dataset. These findings indicate that ResNet50 is effective in identifying multiple retinal diseases from OCT images. The proposed approach has potential applications in computer-aided diagnostic systems to assist clinicians in performing faster and more accurate retinal disease screening and early detection.

Techdoctor Android Application Design for Laptop Damage Diagnosis Based on Symptoms Experienced by Users

2026-06-11T08:48:34+07:00

Laptop damage is a common problem faced by computer users in today’s digital era. Users generally have difficulty identifying the source of damage due to limited technical knowledge. This research aims to design and build an Android application named TechDoctor that helps users perform initial diagnosis of laptop damage independently based on selected symptoms. The application was developed using the Kotlin programming language in the Android Studio environment and applies a rule-based expert system approach with forward chaining inference method. The knowledge base was compiled from literature studies and interviews with experienced laptop technicians, covering common symptoms such as overheating, battery failure, LCD problems, and RAM or hard disk issues. Testing was carried out using the Black Box Testing method with 30 test scenarios, yielding a diagnosis accuracy rate of 86.7%. The results show that the TechDoctor application is capable of providing initial diagnosis and solution recommendations quickly, accurately, and in a manner easily understood by non-technical users.

Deep Learning Edge Detection for Image Segmentation: Advances and Challenges

2026-06-11T08:53:12+07:00

This study offers a thorough Systematic Literature Review (SLR) of current advancements in deep learning-based edge detection techniques for picture segmentation. The study is motivated by the shortcomings of conventional edge recognition methods in processing complicated images, especially when there is significant noise, low contrast, and a variety of texture variations. Deep learning techniques are becoming more and more popular because to the growing need for precise picture segmentation in a variety of industries, including autonomous driving and medical imaging. This study examines 32 carefully chosen scientific papers from reliable sources using the PRISMA 2020 technique. The results show a substantial departure from traditional approaches in favor of Transformer-based models, encoder-decoder models like U-Net, and Convolutional Neural Network (CNN)-based architectures that increase edge detection accuracy and consistency. Additionally, it has been demonstrated that combining attention processes with multi-scale feature extraction improves object border accuracy. Nonetheless, issues including the need for sizable labeled datasets, computational complexity, and restricted generalization capacity continue to be major worries. Future trends toward the creation of more effective, flexible, and real-time models are also identified by this study. It is anticipated that the results will be used as a guide for creating more reliable and useful edge detection techniques.

UI/UX Design and Prototype of the IKJ Advertising Mobile App using the User-Centered Design Method

2026-06-08T14:19:45+07:00

Many companies are transforming their systems from website-based to mobile-based applications. User Experience (UX) is a crucial component in user interaction with applications, given that ease of use must be the top priority of technology. This study aims to design the UI/UX and prototype of the IKJ Advertising mobile application using the User-Centered Design (UCD) method. The design was based on user needs through observation, interviews, and literature review. The resulting prototype was then evaluated using usability testing with the Think-Aloud method, Performance Measurement, and the System Usability Scale (SUS). The evaluation results show that the prototype achieved a SUS score of 91.5, which falls into the Acceptable category, grade A, and Best Imaginable. These results indicate that the application design has an excellent level of usability and is capable of providing an effective, efficient, and easy-to-use user experience. This study produced a UI/UX design and prototype for the IKJ Advertising mobile application that can be further developed as a reference for corporate application development.

Early Detection of Dermatitis Through Comparison of Image Size Variations Using the You Only Look Once (YOLO) Framework

2026-06-09T21:22:39+07:00

Dermatitis is an inflammatory skin disease characterized by symptoms such as redness and itching, requiring early identification to prevent the development of more serious conditions. The use of image processing technology and deep learning is important as a supporting solution in the process of rapid and accurate skin disease detection. This study aims to compare the performance of the You Only Look Once (YOLO) model on several image size variations, evaluate the model's ability to detect types of dermatitis based on precision, recall, and mean Average Precision (mAP) metrics, and determine the optimal number of epochs to improve model performance. The dataset used consisted of 440 images of patients' skin obtained from Dr. Rivai Abdullah General Hospital and augmented to 1,320 images. The data was divided into training, validation, and test data. The YOLOv11 model was trained to detect four types of dermatitis, namely contact dermatitis, atopic dermatitis, static dermatitis, and circumscribed neurodermatitis. The results showed that image size and epoch number affected model performance. The best configuration was obtained with an image size of 640 × 640 pixels and 150 epochs, resulting in a precision value of 0.693 and a recall value of 0.674. These results indicate that the YOLO model has the potential to be used as an effective early identification support system for dermatitis.

Design and Implementation of a Web-Based Used Car Electronic Catalog with Decision Support System Using the Waterfall Model and SAW Method

2026-06-11T08:56:35+07:00

Managing used car inventories poses significant operational challenges for showrooms, particularly regarding stock tracking inefficiencies and subjective multi-criteria evaluations during customer consultations. This study develops a web-based e-catalog and decision-support application engineered using the Laravel framework and MySQL database to streamline showroom operations. The system embeds the Simple Additive Weighting (SAW) method to provide objective, data-driven vehicle recommendations based on five critical operational variables: price, production year, inventory age, tax validity, and transmission type. To preserve operational integrity, the application enforces role-based access control distributed across three distinct user profiles: Marketing, Sales, and Owner. Systematic computational validation confirms the correct algorithmic execution of the backend scoring process, successfully generating automated, mathematical priority rankings that match the system's responsive user interfaces. The implementation proves that integrating a standardized decision-support algorithm into a scalable framework mitigates administrative bottlenecks, removes human subjectivity, and enhances executive data transparency in secondary automotive commerce.

Teaching Speaking Skills using Sensay Web to Require Personal Response

2026-06-11T09:03:53+07:00

Using artificial intelligence in language teaching has opened up new ways to enhance speaking lessons through engaging online tools. This study looks at how Sensay Web is used as a tool with AI to help teach speaking skills by having students respond personally. A descriptive qualitative method was used, which included ten students from the English Education Study Program at Universitas Nias. Data was gathered by observing, writing down notes, looking at the materials created by participants, and using questionnaires to assess their understanding during a training session on how to use Sen say Web for teaching speaking skills. The results show that the participants were able to create speaking activities, handle digital learning resources, and design personal response tasks using Sensay Web. Participants also showed more confidence in using educational technology to help teach speaking skills. Even though there were some technical problems like trouble logging in, issues with microphone permissions, and finding your way around the platform, these problems were fixed with help from guided practice and support from the facilitator. The study finds that Sensay Web offers a good AI-based setting for teaching speaking skills. It helps learners by making them more involved, encouraging them to learn on their own, and allowing them to practice real communication through personal and meaningful responses.

Development of a Python-Based Prepress Application for Document Color and Size Conversion in Print-Ready Files

2026-06-12T12:01:59+07:00

The prepress stage plays a critical role in ensuring the feasibility of design documents before entering the printing process, helping prevent printing defects. However, the conventional checking and processing of print-ready documents are still performed manually, leading to time inefficiency. At the same time, commercially available software in the current industry tends to be costly and possesses complex system architectures. Therefore, this study aims to design a simpler Python-based prepress application to validate color modes and perform automatic paper-size conversions. The method employed is experimental, utilizing a Research and Development (R&D) approach. The program was designed using the Python programming language, leveraging the PyMuPDF library for internal metadata extraction and CustomTkinter for the graphical user interface (GUI) design. Testing of the conversion results was conducted by comparing parameters against Adobe Photoshop, alongside assessing the system's computational processing speed within the application. The results indicate that
the application successfully extracted document metadata, converted color modes into pure CMYK, and adjusted paper dimensions accurately. This study concludes that the developed Python-based preflight application is capable of streamlining the workflow of printready document inspections, minimizing the risk of human error, and enhancing prepress production time efficiency for small to mediumscale printing industries.

Analysis of the Use of Cloud-Based Collaborative Tools (Notion and Google Docs) for Software Planning Optimization: A Literature Review

2026-06-12T12:06:47+07:00

Software planning is a crucial phase in the Software Development Life Cycle (SDLC) that significantly determines project success. However, in practice, development teams often face challenges such as miscommunication, unclear task distribution, and unstructured documentation. The advancement of cloud-based technology offers solutions through collaborative tools such as Notion and Google Docs. This study aims to analyze the utilization of cloud-based collaborative tools in optimizing software planning through a literature review approach. The research employs a qualitative method by examining relevant academic sources, including journals and scholarly publications. The results indicate that Notion plays a role in task management, progress tracking, and project information organization, while Google Docs effectively supports documentation and real-time collaborative writing. The combined use of these tools enhances team communication, clarifies task structure, and improves planning efficiency. Therefore, cloud-based collaborative tools provide a positive contribution to optimizing software planning processes.

Implementation of a Web-Based Sales Information System and Stock Management at TB Solusi Mitra Bangunan

2026-06-12T14:20:07+07:00

Purpose: This study aims to design and implement a web-based information system for sales management and inventory control at Solusi Mitra Bangunan store, to overcome problems caused by manual recording processes. Approach: The research employed the Waterfall software development method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. System modeling was conducted using Unified Modeling Language including use case diagrams, activity diagrams, sequence diagrams, and entity relationship diagrams. The system was developed using CodeIgniter 4 framework and MySQL database. Results: The result is an information system that supports data management of goods, suppliers, stock entry, sales transactions, and report generation automatically. The system was tested using black-box testing and showed valid functionality and accurate data processing. Conclusions: The implementation of this system effectively accelerates transaction processes, minimizes recording errors, ensures inventory data accuracy, and facilitates management in obtaining real-time information for decision making.

Extracurricular Management Information System at SMPN 1 Umbu Ratu Nggay Central Sumba Regency

2026-06-12T17:59:38+07:00

Extracurricular activities play an important role in developing students’ interests, talents, and non-academic skills. At SMP Negeri 1 Umbu Ratu Nggay, extracurricular data management is still carried out using Microsoft Word and Microsoft Excel. Although these applications assist teachers in recording and storing data, several challenges remain, including time-consuming data retrieval, non-centralized information storage, and limited accessibility. This study aims to develop a web-based Extracurricular Management Information System (SIME) to support the management of extracurricular data in a more structured, efficient, and accessible manner. The system is designed to complement, rather than replace, the use of Microsoft Word and Microsoft Excel by providing a centralized platform for storing and managing information. The development process follows the Waterfall methodology and utilizes UML for system modeling. The system is implemented using PHP and MySQL. The results are expected to improve the efficiency of extracurricular administration, facilitate data management, and provide easier access to information for students and school staff. By integrating extracurricular data into a web-based system, information can be managed more effectively and accessed more quickly when needed.

A Decision Support System for Determining the Level of Digital Addiction Among College Students Using the TOPSIS Method

2026-06-13T18:35:54+07:00

Excessive smartphone use among college students has the potential to lead to digital addiction, which can negatively impact academic performance, mental health, and the quality of social life. This issue requires a systematic and objective approach to accurately identify the level of digital addiction. This study aims to develop a decision support system using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the level of digital addiction among students based on five criteria: daily smartphone usage duration, frequency of accessing social media, sleep disturbances caused by gadget use, the impact of smartphones on study focus, and the frequency of gadget use during lectures beyond academic needs. Data were collected via a questionnaire distributed to 30 active students. The results of the TOPSIS calculations showed that 63.3% of students fell into the moderate category, 23.3% into the high category, 10.0% into the low category, and 3.3% into the very high category, with an average preference value of 0.4210. These findings indicate that digital addiction is a real and fairly widespread problem among students. The TOPSIS-based decision support system has proven capable of producing objective and measurable classifications, making it an effective tool for educational institutions in designing targeted intervention programs to address student digital addiction.

Design and Development of the Alhazen Mobile-Based Smart E-Commerce Application for Optics Using the Agile SDLC

2026-06-12T18:03:57+07:00

The growth of e-commerce and the use of mobile devices has driven the need for digitalization in the optical sector, including Alhazen Optics, which still faces limitations in inventory management, transaction recording, order tracking, and marketing. This study aims to design and develop the Alhazen Smart E-Commerce mobile application using the Agile Software Development Life Cycle approach. The research method employs a descriptive qualitative approach through structured interviews and direct observation to identify the system’s functional and non-functional requirements. The development stages include requirements analysis, use case design, activity diagrams, class diagrams, interface design, implementation, and scenario-based functional testing. The resulting application integrates features such as registration and login, product catalog, frame and lens selection, eyeglass prescription entry, shopping cart, payment, order tracking, profile management, inventory management, and consultation with an administrator. Test results show that all major scenarios passed, indicating that the application functions as designed. With this integrated system, customers can place orders more easily, while administrators can centrally and efficiently monitor products, transactions, consultations, and shipping status. This application is deemed functionally suitable to support the sales process, improve data management consistency, expand service access, and provide more transparent order information. Further development is recommended to include usability, security, and performance testing, as well as minimum stock notifications, sales analytics, and AI-based virtual try-on features.

Decision Support System for Classifying Bullying Victims Using Website-Based C4.5 Algorithm

2026-06-13T18:42:46+07:00

Bullying remains a serious problem in Indonesian schools, where Indonesia ranks highest in ASEAN with a bullying rate of 84%. Early detection of bullying victims is critical for timely intervention by school counselors (BK teachers). This study develops a web-based Decision Support System (DSS) that integrates the C4.5 decision tree algorithm to classify students at risk of becoming bullying victims. The system uses the Bullying 2018 with Labels dataset from Kaggle containing 22,766 student records with eight input attributes and four output classes: Ringan (Mild), Sedang (Moderate), Berat (Severe), and Sangat Berat (Very Severe). The C4.5 model is implemented in Python using scikit-learn with entropy criterion and validated through 10-fold cross validation, achieving an accuracy of 99.94%. The model is deployed as a Flask REST API and integrated with a PHP CodeIgniter 3 web frontend and MySQL database. The system allows BK teachers to input student behavioral data and receives instant risk classification results. Usability testing confirmed the system is effective, efficient, and satisfying to use. This DSS provides an objective, data-driven tool to support early identification and handling of bullying cases in schools.

Implementation of the Vigenere Cipher Algorithm in a Python GUI-Based Text Encryption App

2026-06-15T12:54:23+07:00

Rapid advances in information technology have increased the risk of data theft and misuse in digital communications. Cryptography is a scientific solution for ensuring the confidentiality, integrity, and authenticity of data. The Vigenère Cipher is one of the best-known classical cryptographic algorithms, developed by Blaise de Vigenère in the 16th century, which uses a polyalphabetic substitution method based on a repeating key and falls under the category of symmetric cryptography. This research implements the Vigenère Cipher algorithm in a desktop application named VigCrypt, built using the Python 3 programming language and the Tkinter framework as the graphical user interface (GUI). Unlike previous research, which mostly implemented the Vigenère Cipher on web- or mobile-based platforms, this study focuses on a desktop application that serves not only as an encryption tool, but also as a medium for learning cryptography. The developed application features text encryption and decryption, visualization of the calculation steps for each character—displaying numerical values and modular calculations explicitly—a 26x26 Vigenère Square table as a visual reference, and the ability to export results to a .txt file. Functional testing was conducted on 10 plaintext samples covering various text types, including short text, sentence-length text, and alphanumeric text. The test results showed a 100% accuracy rate for both encryption and decryption, with all non-alphabetic characters such as spaces, numbers, and punctuation marks—successfully preserved.

Implementation of the K-Means Clustering Algorithm Based on CRISP-DM Using Orange for Sales Product Grouping in Shopee.

2026-06-15T12:57:18+07:00

The growth of e-commerce in Indonesia, especially on Shopee, has generated large product data that are not yet fully used by sellers in decision-making. This study aimed to cluster Shopee product sales data based on physical characteristics and product information completeness using the K-Means Clustering algorithm within the CRISP-DM framework through Orange Data Mining. The dataset included 4,997 product records with eight attributes: product category, product name length, product description length, number of product photos, product weight, and product length, height, and width. After data cleaning, 4,895 records were analyzed. The results identified three clusters C1 contained large and heavy products, C2 contained products with general and dominant characteristics, and C3 contained relatively small to medium products. The silhouette coefficient score was 0.238, indicating a weak cluster structure, but the model still provided an overview of product grouping based on physical characteristics.

Delay Performance Analysis of a Multi-Client ESP32 Soft-AP IoT Network Using an Adaptive Transmission Interval Method

2026-06-15T12:59:56+07:00

This study proposes a delay-based Adaptive Transmission Interval (ATI) method to improve communication performance in IoT networks utilizing an ESP32 Multi Client SoftAP architecture. The system consists of one ESP32 functioning as an Access Point and gateway, and three ESP32 client nodes that transmit sensor data from DHT22 sensors via a local Wi-Fi network. Network performance was evaluated using Quality of Service (QoS) parameters, including delay, packet loss, and Packet Delivery Ratio (PDR). The ATI method dynamically adjusts the data transmission interval based on the measured Round Trip Time (RTT) values.

Experimental results obtained from a 30-minute testing period showed that 5,391 out of 5,400 data packets were successfully received, resulting in a packet loss rate of 0.17% and a PDR of 99.83%. Furthermore, ATI successfully reduced the average delay from 245.77 ms to 140.10 ms, representing a 42.99% improvement in communication performance compared to the conventional Fixed Transmission Interval (FTI) method. These findings demonstrate that ATI is effective in reducing network congestion, enhancing communication stability, and optimizing ESP32 performance in multi-client IoT environments.

Student's Perceived Impact of Artificial Intelligence on Their Academic Assignment Behaviors: A Qualitative Study Among Electronics Engineering Students

2026-06-15T17:46:24+07:00

In higher education, the rapid advancement of Artificial Intelligence (AI) technology has significantly transformed the way students approach learning and finish their assignments. Students are increasingly relying on AI-powered tools such as ChatGPT, Gemini, and other generative AI applications to assist with tasks like gathering information, developing ideas, solving problems, and completing assignments. While these devices offer numerous educational benefits, concerns have emerged regarding their potential impact on students’ academic honesty, ability to think critically, and study habits. The aim of this study is to explore how students studying Electronics Engineering view the impact of artificial intelligence on their approach to completing academic assignments. To fully understand students’ perspectives and experiences regarding the use of AI in academic environments, a qualitative research approach was employed. Six students majoring in Electronics Engineering took part in semi-structured interviews to collect data. The gathered information was then analyzed using thematic analysis, a process that involved reducing the data, organizing it into patterns, and preparing the final conclusions. Four key themes came up from the analysis: AI as a tool to support learning, the boost in productivity and efficiency when completing tasks, concerns about becoming too dependent on AI and losing critical thinking skills, and ethical issues related to academic honesty and the originality of work. The findings indicate that students generally see AI as a helpful tool that enhances learning effectiveness, helps finish assignments more quickly, and makes course content easier to grasp. Participants knew about the potential risks of depending too much on AI, including reduced motivation for independent learning and challenges in maintaining proper ethical standards in academic work. The study concludes that AI significantly and intricately influences how students approach their academic assignments. It raises important ethical and responsible usage concerns while simultaneously providing considerable educational advantages. Therefore, to ensure that AI functions as a supportive learning aid that boosts rather than diminishes students’ critical thinking and ability to learn independently, higher education institutions must establish clear guidelines and promote digital literacy.

Design and Implementation of the Vocaseek Platform as a Web-Based Internship Recruitment and Student Talent Management Information System Using React and Laravel

2026-06-15T17:49:52+07:00

Perkembangan teknologi informasi telah mendorong transformasi digital di berbagai bidang, termasuk proses rekrutmen magang dan manajemen talenta mahasiswa. Namun, proses pencarian magang dan seleksi kandidat masih sering dilakukan secara manual melalui berbagai platform yang tidak terintegrasi, sehingga menimbulkan kesulitan bagi mahasiswa maupun perusahaan. Studi ini bertujuan untuk merancang dan mengimplementasikan platform Vocaseek sebagai sistem informasi rekrutmen magang dan manajemen talenta mahasiswa berbasis web. Sistem ini dikembangkan menggunakan React dan Vite pada frontend dan Laravel sebagai backend berbasis REST API. Platform ini mendukung empat jenis pengguna: pelamar, perusahaan mitra, staf admin, dan super admin. Fitur utama yang disediakan meliputi pendaftaran dan verifikasi akun, manajemen profil pelamar, pra-tes karakter kerja, manajemen lowongan, manajemen kandidat, verifikasi perusahaan, dan manajemen administrasi sistem. Hasil implementasi menunjukkan bahwa Vocaseek mampu mengintegrasikan seluruh proses rekrutmen magang ke dalam satu platform terpusat. Sistem ini memudahkan mahasiswa untuk menemukan peluang magang dan membantu perusahaan dalam menyeleksi kandidat secara lebih efektif dan efisien.

Diet Package Profit Optimization of a Fitness Center Using the Simplex Method and POM-QM Software

2026-06-04T21:10:03+07:00

The growing trend of healthy lifestyles has encouraged fitness centers to provide supporting services in the form of healthy meal packages tailored to customers’ nutritional needs. This study aims to optimize the profit of diet package production at a fitness center using the simplex method and POM-QM software. The research employed a descriptive quantitative approach using secondary data consisting of profit per package, resource requirements, and available resource capacities. The study focused on two types of diet packages, namely the High-Protein Package and the Balanced Package, with limited resources of 500 kg of food ingredients, 2,000 hours of nutritionist consultation time, and 1,500 hours of kitchen capacity per month. The mathematical model was formulated into an objective function and constraint functions, then solved manually using the simplex method and validated using POM-QM for Windows software. The results showed that the optimal solution was achieved by producing 100 High-Protein Packages and no Balanced Packages. This combination generated a maximum profit of Rp25,000,000 per month. Both manual calculations and POM-QM software produced the same results, indicating that the simplex method is capable of providing accurate optimal solutions. In addition, the use of POM-QM software accelerated the analysis process, minimized calculation errors, and facilitated the visualization of optimization results. Therefore, the simplex method and POM-QM software are effective tools for decision-making in determining the optimal production strategy for diet packages at fitness centers.

Designing a Simple QR Code Based Digital Traceability System for Halal Product Transparency

2026-05-28T16:38:41+07:00

The halal product industry in Indonesia continues to grow rapidly, but consumers still face limitations in independently verifying a product's halal status solely from packaging labels. This study aims to design a simple QR Code-based digital traceability system to transparently convey halal product information and evaluate the system's acceptance based on consumer perceptions. The study used a sequential, exploratory, mixed-methods approach, with a case study of the Wafello Chocolate Gellato Style wafer product (PT Mayora Indah Tbk). The system was designed in three stages: product information content development, QR Code creation using a QR Code generator, and system finalization. Testing was conducted with 30 respondents using a questionnaire based on the Technology Acceptance Model (TAM) theory, expanded with trust and perceived risk dimensions. The results showed that the system was very well received, with an average positive dimension score of 4.24 on a scale of 5.00. Perceived benefits and perceived system needs received the highest scores (4.36 each), followed by ease of use (4.30), trust (4.15), and intention to use (4.03). However, respondents expressed serious concerns about the risk perception related to potential data manipulation and reliance on an internet connection (mean = 4.17). This study concluded that a QR code-based digital traceability system is technically feasible, low-cost, and relevant for halal product consumers. Further development is recommended, including real-time integration with the BPJPH/BPOM database, the addition of digital encryption, and the exploration of blockchain technology to enhance system security and reliability.

Conceptual Model of Integrating Qur'ani Values in Digital Traceability Systems to Support Sustainable Halal Industry

2026-06-03T13:10:05+07:00

The global halal industry faces fundamental challenges in terms of supply chain reliability, especially when the existing verification system is not fully able to guarantee product integrity from upstream to downstream in a transparent and accountable manner. This article offers a conceptual model that formulates the three main values of the Qur'an, namely 'adl (justice), amanah (trust), and maslahah (public welfare), as applied operational principles that are integrated into the halal industry digital traceability system. Using a qualitative-conceptual research approach that relies on library research methods, systematic literature review, and theoretical framework analysis, this study builds a layered model architecture that includes five functional layers: input, processing, storage, output, and governance. Each layer is organically linked to one or more Qur'anic values as a normative foundation as well as a technical guide. The results of the analysis show that these three values are not just ethical constructs that are symbolic, but have a strong structural conformity with the working principles of blockchain technology, the Internet of Things (IoT), artificial intelligence (AI), and decentralized data management systems. The resulting model has been tested for feasibility through a conceptual validation process based on triangulation of experts from the fields of sharia, information technology, and the halal food industry. These findings contribute to the development of an ethical-technical framework that can be operationalized by regulators, business actors, and digital platform developers in a sustainable halal ecosystem.

Development of E-Archive Using Sequential Searching and Interpolation Searching Algorithms at PT. Spidest Internasional

2026-06-16T22:46:43+07:00

Manual archive management at PT. Spidest Internasional causes operational problems, including delays of 5–20 minutes in finding pilgrim documents, high data loss risks, and administrative inefficiency. This research develops a web-based E-Archive information system integrating Sequential Searching and Interpolation Searching algorithms to improve search speed and accuracy for pilgrim data. The system covers six main modules: pilgrim data management (add, edit, delete), name- and NIK-based search, departure management with date search, digital document archiving, reports, and admin authentication. System design is supported by UML artifacts (Use Case, Activity, and Class Diagrams) and UX-principled interface design. Development follows the Waterfall methodology. Testing employed Black Box Testing (29 scenarios across 6 modules) and User Acceptance Testing (UAT) with 15 respondents. Results show Interpolation Searching is 87.7% more efficient than Sequential Searching on 60 records, reaching 95.4% efficiency on 100 records. Black Box Testing achieved a 100% pass rate, and UAT recorded an 89.4% average user satisfaction score. This research contributes an efficient digital archive solution for Umrah and Hajj travel companies

Trends, Challenges, And Future Directions of Semantic Segmentation Based on Deep Learning

2026-06-16T22:44:09+07:00

Semantic segmentation is a fundamental task in computer vision that classifies each pixel in an image into a specific category. Advances in deep learning have significantly improved semantic segmentation performance across various applications, including medical imaging, remote sensing, autonomous driving, and industrial inspection. This study aims to analyze the development of methods, architectures, challenges, and future research directions in deep learning-based semantic segmentation. A Systematic Literature Review (SLR) was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Literature was collected from the SCOPUS database using keywords related to deep learning-based semantic segmentation. A total of 5,867 publications were identified, and 30 studies were selected after applying predefined inclusion and exclusion criteria. The review found that Convolutional Neural Networks (CNNs), Vision Transformers, and hybrid architectures are the dominant approaches. Attention mechanisms and multi-scale feature extraction were also identified as effective techniques for improving segmentation performance. Despite these advancements, challenges such as class imbalance, small object segmentation, and the need for large annotated datasets remain unresolved. The findings provide a comprehensive overview of current trends and highlight potential directions for future research in semantic segmentation.

Development of a Web-Based Employee Task and Evaluation System Using the Prototype Method

2026-06-16T22:47:50+07:00

Administrative activities in employee management require a system that can record tasks, attendance, clock-out time, and sick leave or permission requests in a structured manner. Manual recording can slow down data retrieval, increase the risk of errors, and make reporting less effective. This study was designed and developed a web-based employee task and performance evaluation application for Bank Jatim Cabang Pembantu Berbek using the prototype method. The development stages consisted of requirement identification, initial planning, system design, prototype development, evaluation, improvement, and final implementation. The application provides three access roles, namely administrator, management, and employee. The main features include login, registration, account approval, employee data management, attendance, clock-out time recording, sick leave or permission request submission with supporting evidence, task management, reports, announcements, notes, notifications, and performance visualization. System validation was conducted using Black-Box Testing and User Acceptance Testing involving 15 respondents. The UAT result reached 683 out of 750 points, equal to 91.07%, and was categorized as very good. Therefore, the proposed application is feasible for supporting employee task management, attendance, leave administration, and performance evaluation processes in a more efficient and documented manner.

Analysis of the Influence of Training on Civil Servant Competency Using Apriori Algorithm

2026-06-17T14:09:28+07:00

Improving the competence of the State Civil Apparatus (ASN) is one of the important factors in supporting the effectiveness of the performance of government organizations and improving the quality of public services to the community. Good ASN competencies can help achieve organizational goals optimally. One of the efforts made to improve the competence of ASN is through the implementation of various training programs that are tailored to job needs and technological developments. However, not all training programs implemented are able to have an optimal impact on improving the competence of ASN. Therefore, an analysis is needed to find out the relationship between the training followed and the competencies produced. This study aims to analyze the influence of training on ASN competencies and apply an A priori algorithm in finding patterns of relationships between the type of training, the field of training, the level of relevance, and the competence of ASN in the Binjai City BKPSDM. The method used in this study is data mining with the association rule technique using an A priori algorithm. The ASN training data used comes from the Binjai City BKPSDM and is processed using the RapidMiner application. The analysis process was carried out based on support, confidence, and lift ratio values to produce association rules that show the relationship between research variables. The results showed that the best association rule obtained was "If the Relevance Level is Very Relevant, the Competence is Very Good" with a support value of 54%, confidence of 100%, and a lift ratio of 1.54. These results show a strong relationship between the level of relevance of training and the improvement of ASN competence. The more relevant the training provided to the ASN field of duties, the higher the competence that can be achieved. Thus, the A priori algorithm can be used effectively to analyze the influence of training on ASN competencies and assist the Binjai City BKPSDM in developing a training program that is more targeted, effective, and in accordance with the needs of ASN competency development.

Implementation of Queue Tree for Network Quality of Service (QoS) Optimization at MIN 3 Pontianak

2026-06-18T16:46:51+07:00

Optimal network performance is essential to support administrative and learning activities in educational institutions. MIN 3 Pontianak experienced network degradation due to the use of a single bandwidth channel shared by all users, resulting in inefficient traffic management and uneven bandwidth distribution. This study aims to optimize network Quality of Service (QoS) through bandwidth management using the Queue Tree method on a MikroTik router. The research method includes network requirement analysis, traffic classification based on user categories, Queue Tree configuration, and performance evaluation using throughput, latency, jitter, and packet loss parameters. The implementation results show that bandwidth allocation into several service classes, such as examination and teacher classes, improves bandwidth efficiency and minimizes interference among users. QoS testing indicates increased throughput, lower latency, and more stable jitter compared to the previous network condition. Therefore, the Queue Tree method is effective in optimizing network service quality and improving network performance at MIN 3 Pontianak.

Analysis and Design of Web-Based E-Commerce Information System at Riskha's Kimbab

2026-06-18T16:53:15+07:00

The development of information technology encourages business actors to utilize digital-based systems to support business processes, including in the sales sector. The use of E-commerce systems also has a positive impact on the development of Micro, Small, and Medium Enterprises (MSMEs). Riszkha's Kimbab is a culinary MSME business engaged in the sale of Korean food, specifically kimbab and various other typical Korean food menus. This business is located in the environment of the Indonesian Development University (UNIPI) and has quite large market potential because its target consumers are dominated by students, lecturers, and the surrounding community who have an interest in modern culinary. Based on the research results, the Riszkha's Kimbab e-commerce website developed in this study has been successfully built and implemented well, and is able to accommodate the Pre-Order (PO) business process more effectively, structured, and in accordance with the operational needs running on the sales system. The dynamic PO schedule management feature provides flexibility for administrators in managing the system. The implementation of automation features in the system, such as automatically reducing product stock during the checkout process and increasing the number of products sold when the order has been processed, contributes significantly to reducing the manual workload of administrators.

Predicting Chili Pepper Diseases using a Decision Tree in an Android-Based Internet of Things (IOT) Monitoring System

2026-06-19T08:53:57+07:00

Chili pepper plants are susceptible to diseases caused by changes in the microclimate, making a data-driven monitoring system essential. This study designed an Internet of Things (IoT) system to monitor the microclimate and predict disease risks in chili pepper plants via an Android app. The system uses an ESP32 connected to a DHT22 sensor, a capacitive soil moisture sensor, a BH1750 sensor, a rain sensor, and a DS3231 RTC. Data on air temperature, air humidity, soil moisture, light intensity, and rainfall conditions are sent to the Firebase Realtime Database via WiFi in real-time. Predictions are made using the CART Decision Tree algorithm with low, medium, and high risk classifications. Test results show that the model achieved an accuracy of 95%, precision of 96%, recall of 95%, and an F1-score of 95%, with 19 out of 20 test data points correctly classified. This system helps farmers make cultivation decisions more quickly and objectively based on actual environmental conditions in chili farming fields.

Optimization of New Member Registration Services Through a Web-Based System Using the RAD Method at CU Keling Kumang BO Karangan

2026-06-19T13:36:18+07:00

Keling Kumang Credit Union (CU) in Karangan is a cooperative-based financial institution that provides membership services to the public. The conventional new member registration process makes data management ineffective and time-consuming. Therefore, a web-based information system is needed to optimize the new member registration process. The method used in developing this system is Rapid Application Development (RAD), which consists of requirements planning, design, construction, and implementation. Data collection techniques used in this study included observation, interviews, and literature review. The system was built using the Laravel framework, the PHP programming language, and a MySQL database. The results show that the E-Kelimang system facilitates prospective members in registering online, uploading required documents, and assists the administrator in verifying and managing member data. This system makes the member registration process more effective, efficient, and organized, and improves the quality of service at Keling Kumang Credit Union (CU) in Karangan.

IoT-Based Monitoring System for pH and Electrical Conductivity of Hydroponic Nutrient Solutions

2026-06-20T16:08:08+07:00

Technological advancements in agriculture have encouraged the adoption of hydroponic systems as an efficient cultivation method for overcoming land limitations. In hydroponic cultivation, pH and Electrical Conductivity (EC) are essential parameters that influence nutrient availability and absorption by plants. However, monitoring these parameters is often performed manually, making the process inefficient and prone to measurement errors. This study aims to design and implement an Internet of Things (IoT)-based monitoring system for pH and EC in hydroponic nutrient solutions to provide real-time monitoring. The system utilizes an ESP32 microcontroller integrated with a pH sensor, EC sensor, Analog Signal Isolator, I2C LCD, and the Blynk application for data visualization. The research methodology includes hardware design, software development, system implementation, and sensor accuracy evaluation. Experimental results show that the pH sensor achieved an accuracy of 89.07%, while the EC sensor achieved an accuracy of 92.26%. The developed system successfully displayed pH and EC measurements in real time through both the I2C LCD and Blynk application, enabling remote monitoring of nutrient solution conditions. Therefore, the proposed system is suitable as an IoT-based monitoring tool to support nutrient management in hydroponic cultivation.

Decision Support System for the Selection of Prospective Recipients of the Smart Indonesia Program (PIP) Assistance using the Topsis Method

2026-06-18T16:50:29+07:00

This study aims to implement a Decision Support System (DSS) using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to determine the recipients of the Indonesia Smart Program (PIP) at SMPNegeri 36 Medan. The TOPSIS method was chosen for its ability to produce objective and measurable results based on five main criteria: parents’ occupation (C1), number of dependents (C2), parents’ income (C3), student status (C4), and distance from home to school (C5). Data were collected from 306 students, with assigned weights of 25% for income, 25% for occupation, 20% for dependents, 20% for student status, and 10% for distance.The research stages included scoring, decision matrix formation, data normalization, weighted normalization, and determining positive and negative ideal solutions to obtain preference values (Vᵢ). The results show that students with V ≥ 0.5 are categorized as eligible, while those with V < 0.5 are not eligible. Out of 306 students, 103 were eligible and 203 were not. The implementation of the TOPSIS method in DSS effectively assists schools in selecting PIP recipients more quickly, objectively, and accurately, ensuring that aid is given to students who truly need it.

Deep Learning-Based Image Fusion: A Systematic Literature Review on Trends, Datasets, Evaluation Methods, and Research Challenges

2026-06-21T16:09:24+07:00

Advances in digital image processing technology have increased the need for image fusion techniques to produce more accurate and high-quality visual information. This study aims to analyze methodological developments, research trends, datasets, evaluation methods, and challenges in image fusion research through a Systematic Literature Review (SLR) using the PRISMA framework. The literature selection process resulted in 335 papers being included for analysis. The findings indicate that deep learning-based methods, particularly Convolutional Neural Networks (CNNs), have become the dominant approach in modern image fusion research. Furthermore, recent studies have increasingly explored Generative Adversarial Networks (GANs), Transformers, and hybrid methods to improve fusion performance. The most significant challenges identified include the need for large-scale datasets, high computational complexity, the lack of standardized evaluation frameworks, and limitations in model generalization. These findings provide a comprehensive overview of current developments and highlight future research opportunities in deep learning-based image fusion.

pH Monitoring and Nutrient Control in IoT-Based Hydroponic Water Storage Tanks Using ESP 32 Microcontroller

2026-06-22T14:19:12+07:00

Hydroponic plants are a soilless farming method that is increasingly popular due to its efficiency in using resources and increasing crop yields. One important aspect of hydroponic cultivation is monitoring and controlling pH and nutrients in plant nutrient solutions. In this research, researchers developed a pH and nutrient monitoring system as well as an automatic nutrient addition controller in hydroponic reservoirs using a TDS meter sensor, pH meter, and Internet of Things (IoT) technology with an ESP32 microprocessor. The aim of the research is to monitor pH, nutrients, and automatically control nutrients in real-time so that farmers are more efficient in monitoring. The ESP32 microprocessor is used as the brain of the system which can measure the pH and nutrient levels in the nutrient solution. Data generated by the ESP32 microprocessor is sent via the WiFi network to the Firebsae website for remote monitoring. The research results show that this monitoring system is able to measure and monitor pH and nutrient levels accurately. The conclusion from this research is that the automatic monitoring and controller tool designed can work well with sensor reading accuracy reaching 90-95%. With this tool, farmers become more efficient in monitoring and plant growth becomes more because nutritional needs are always met.

Voice Deepfake Detection Using Spectrogram Features and Mel-Frequency Cepstral Coefficients with a Combination of Convolutional Neural Network and Recurrent Neural Network

2026-06-23T22:52:26+07:00

The rapid development of artificial intelligence technology has driven the emergence of voice deepfakes that are increasingly realistic and difficult to distinguish from genuine human speech. This condition creates significant risks, including identity misuse, fraud, and information manipulation, thereby requiring an effective detection system capable of identifying manipulated voice patterns with high accuracy. This study aims to develop a voice deepfake detection system by combining Convolutional Neural Network (CNN) and Recurrent Neural Network (BiLSTM) methods. Audio features were extracted using Mel-Frequency Cepstral Coefficients (MFCC) and spectrograms to capture both frequency characteristics and temporal dynamics simultaneously. All genuine and deepfake voice recordings underwent preprocessing stages, including noise reduction, normalization, segmentation, and augmentation, to improve data diversity and model robustness. The model was evaluated using several data split ratios to determine the most optimal performance. The best result was achieved with an 80:20 ratio, reaching an accuracy of 99.3% and an AUC value of 0.9999. These results demonstrate the model’s strong capability to identify subtle structural changes in audio signals that are difficult to detect using conventional methods. Based on these findings, the CNN–RNN (BiLSTM) approach proved to be highly effective for detecting manipulated voice recordings. This research provides an important contribution to the development of audio security systems and the mitigation of risks associated with the misuse of deepfake technology across various sectors.

Design and Development of an Android-Based E-Commerce Application for A&Y Frozen Food Store

2026-06-22T14:21:14+07:00

The shift in public shopping patterns toward digital services drives frozen food retailers to provide more practical ordering media. At A&Y Frozen Food Store, ordering is still conducted via WhatsApp and direct purchases, leaving transaction data, stock, and order status unmanaged centrally. This condition causes disorganized records, non-transparent stock information, and manual reporting. This study aims to design and build an Android-based mobile e-commerce application for A&Y Frozen Food Store to make the sales process more digital, structured, and efficient. The system development uses the Agile method, covering planning, design, sprint development, testing, evaluation, implementation, and maintenance. The system consists of an Android mobile application for customers (from registration to order tracking) and an admin website for the store (data management and reporting). The results show that the application successfully integrates and computerizes the sales process. Testing using the black-box method on main features yielded valid results, indicating the application runs according to system requirements. Thus, this application enhances transaction efficiency, minimizes recording errors, simplifies data management, and improves customer service quality.

Implementing Automatic Incremental Backups Using Rsync on Debian 12

2026-06-23T22:59:09+07:00

Data is a critical asset whose availability and security must be safeguarded within an information system. Data loss can occur due to hardware failure, user error, or system failure, all of which can disrupt an organization’s operations. Therefore, a data backup mechanism capable of ensuring the availability of information is necessary.This study aims to implement an automatic incremental backup system using rsync on the Debian 12 operating system. The system is built using two servers—a primary server and a backup server—connected via a network. Data synchronization is performed using rsync over the SSH protocol, while the backup automation process is managed using cron.Test results show that rsync is capable of synchronizing data efficiently by transferring only files that have changed. Additionally, cron allows the backup process to run automatically according to a predetermined schedule. The system developed can enhance data security, simplify the backup process, and reduce the risk of data loss on the server.

A Monetization Model for a Telemedicine Platform Based on Customer Segmentation using the K-Means Algorithm and Willingness-To-Pay

2026-06-23T23:03:42+07:00

Telemedicine platforms in Indonesia face post-pandemic monetization challenges, where the freemium model has not been effective in converting free users into paying customers. This study aims to design a customer segmentation-based monetization model using the K-Means Clustering algorithm and Willingness-to-Pay (WTP) analysis in the suburban area of Kuningan Regency. Data were collected through a survey of 264 respondents and analyzed using K-Means with a Silhouette Score evaluation (0.42 at *k*=3) and the Kruskal-Wallis test for differences in WTP between segments. The clustering results identified three customer segments: the Digital Light Generation, Health Care Professionals, and Chronic Patients Needing Monitoring, with significantly different WTP (p<0.001). Revenue simulations recommended an optimal tiered subscription model: Basic Package Rp25,000/month, Premium Rp75,000/month, and Family Rp130,000/month. This model is able to maximize revenue while maintaining inclusiveness of service access, bridging business sustainability and affordability for suburban communities, and contributing to the Precision in Health Care approach in developing digital health businesses..

Evaluation of the E-Open Population Service Information System using COBIT 2019 at DISDUKCAPIL Bekasi City

2026-06-22T14:10:12+07:00

This study aims to evaluate the capability condition of the e-Open information system using the COBIT 2019 framework, focusing on the DSS01, DSS02, DSS05 and DSS06, on the Department of Population and Civil Registration. The study applied a quantitative descriptive approach through questionnaires distributed to employees directly involved in operating and managing the e-Open system using purposive sampling, resulting in 41 valid respondents. All evaluated domains successfully reached Level 4 (Quantitative Process) with a ‘Fully Achieved’ rating. DSS06 scored the highest at 4.50 (90.00%). DSS02 and DSS05 at 4.48 (89.60%) and followed by DSS01 domain at 4.41 (88.20%). To hit the target Level 5, the system still faces a capability gap between 0.50 and 0.59. This study suggests periodic account verification, structured incident logs, and automatic data masking to improve security and governance.

Development of a Web-Based Booking Portal System for a Hospital Information System

2026-06-24T16:10:32+07:00

The advancement of information technology has accelerated digital transformation in the healthcare sector through the development of web-based healthcare services that are more accessible to the public. One implementation of this transformation is a Patient Portal that provides various digital healthcare services, including online appointment booking. However, complex navigation and poorly structured information presentation can reduce user convenience when accessing these services. This study aims to develop a web-based Patient Portal user interface to improve accessibility to digital healthcare services. The research employed the Prototyping method, which includes requirement analysis, interface design, system implementation, data integration, functional testing, and evaluation. The system was developed using React JS, Remix JS, TypeScript, and Mantine UI, and integrated with an Application Programming Interface (API) to provide dynamic data presentation. The development focused on the appointment booking feature, which consists of branch selection, clinic selection, doctor selection, payment method selection, schedule selection, and booking confirmation. The results indicate that the developed interface provides more structured information, simplifies user navigation, and delivers a responsive user experience across various devices. Black Box Testing results showed that all system functions operated according to user requirements. Therefore, the development of the web-based Patient Portal interface can improve access to digital healthcare services and support a more effective and efficient healthcare appointment booking process.

Sentiment Analysis of Lau Berte Waterfall Using the Naïve Bayes Algorithm

2026-06-25T11:26:40+07:00

Social media platforms have become important sources of public opinion, allowing users to share experiences and perceptions regarding tourist destinations. One of the platforms widely used for this purpose is TikTok, where visitors frequently express their opinions through comments on video content. This study aims to implement the Naïve Bayes algorithm for sentiment analysis of TikTok comments related to Lau Berte Waterfall and evaluate its performance through a Streamlit-based web application. A total of 634 comments were collected and processed through several text preprocessing stages, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed using a lexicon-based approach, followed by TF-IDF weighting and Naïve Bayes classification. The developed application provides functionalities for sentiment prediction, performance evaluation, and visualization of sentiment analysis results. Model evaluation in the Streamlit environment achieved an accuracy of 77%, a precision of 80%, a recall of 77%, and an F1-score of 76%. Sentiment distribution analysis revealed that positive sentiment dominated with 64.6% of the comments, followed by negative sentiment at 22.0% and neutral sentiment at 13.4%. These findings indicate that the proposed system is capable of effectively classifying public sentiment and providing valuable insights into visitor perceptions of Lau Berte Waterfall, which may support tourism evaluation and development efforts.

Design and Implementation of an IoT-based Automatic Waste Sorting System using ESP-32, Proximity Sensors, and Firebase

2026-06-25T11:30:31+07:00

Waste management remains a significant environmental challenge due to the low level of public awareness in sorting waste according to its type. Improper waste segregation reduces recycling efficiency and increases the amount of waste disposed of in landfills. This study proposes the design and implementation of an Internet of Things (IoT)-based automatic waste sorting system using an ESP32-S microcontroller. The system is capable of classifying waste into three categories, namely metal, organic, and inorganic waste. Waste identification is performed using a combination of photoelectric, inductive proximity, and capacitive proximity sensors, while HC-SR04 ultrasonic sensors are utilized to monitor the capacity of each waste compartment. The sorting mechanism is controlled by MG996R servo motors, and the collected data are transmitted to Firebase Realtime Database for real-time monitoring through a Kodular-based Android application. The system was developed using the Agile methodology and evaluated through black-box testing. Experimental results show that the proposed system achieved a maximum classification accuracy of 85%. In addition, the ultrasonic sensor demonstrated stable distance measurement performance with an average error below 0.6 cm. The integration of Firebase and Kodular enabled real-time monitoring of waste classification results and bin status. Therefore, the proposed system can improve waste management efficiency and has potential applications in smart waste management for both household and public environments.

Prediction of the Number of Motor Vehicle Inspections using the Long Short-Term Memory (LSTM) Method as a Decision-Support Tool at the Transportation Agency of Langkat Regency

2026-06-26T18:41:13+07:00

Sustained population growth increases the demand for public services, including motor vehicle inspection or periodic testing (KIR). The Transportation Agency of Langkat Regency faces difficulty in accurately predicting the number of vehicles to be tested in a given period, which causes queue build-ups and inefficiency in resource allocation. This study aims to design and implement a prediction model for the number of motor vehicle inspections using the Long Short-Term Memory (LSTM) method as a decision-support tool. The data used are monthly historical data for the period from January 2023 to December 2025, comprising 36 data points. The data were normalized using Min-Max Scaling, formed into sequential samples with a timestep of three, and then divided into 80% training data and 20% testing data. The model was evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. The evaluation obtained an MAE of 3, an MSE of 9, and an RMSE of 3 vehicles per month, indicating a high level of accuracy. The model projects a total of 916 vehicles in 2026 and 986 vehicles in 2027, with the testing peak occurring in July. These results can be used as a basis for resource planning and for improving the quality of public services.

Design and Build of an Automatic Cat Litter Box Cleaner Based on Internet of Things with Weight Detection

2026-06-26T18:55:21+07:00

This research aims to design and implement an Internet of Things (IoT)-based Cat Litter Box that can automatically clean the litter box based on weight detection. The system uses an ESP8266 microcontroller as the main controller, which is connected to the Firebase Realtime Database and an Android application. A load cell sensor is used to detect the weight of the waste, while an infrared obstacle sensor functions to detect the presence of the cat. When the detected weight is ≤ 500 grams and no object is detected, a stepper motor controlled by a DRV8825 driver activates the cleaning mechanism, and an MG996R motor opens and closes the waste cover. The sensor data and system status are sent to Firebase and displayed through the Android application. Based on testing, the system worked properly. The device was able to perform automatic cleaning according to sensor conditions, display sensor data on the application, and send notifications when the detected weight exceeded 500 grams or when the infrared sensor detected an obstruction for a certain duration.

Analysis of Human Resource Quality in Maintaining Customer Satisfaction at Kopi Kenangan in Pamekasan through the Total Quality Management Approach

2026-06-23T11:42:40+07:00

This study aims to analyze the quality of human resources (HR) in maintaining customer satisfaction at Kopi Kenangan in Pamekasan through the Total Quality Management (TQM) approach. This study is motivated by the increasing competition in the coffee shop business, which requires companies to be able to provide quality service to maintain customer satisfaction. In the service industry, HR quality is a critical factor because employees interact directly with customers and influence the quality of service provided.

The research method used is a qualitative descriptive method. Data collection techniques were conducted through observation, interviews, documentation, and literature review. The research subjects consisted of managers, employees, and customers of Kopi Kenangan Pamekasan. Data were analyzed using the Miles and Huberman data analysis technique, which includes data reduction, data presentation, and drawing conclusions.

The research results indicate that the quality of human resources at Kopi Kenangan Pamekasan is already quite good. This is evident from the employees’ communication skills, friendly attitude, work discipline, and ability to collaborate as a team in providing service to customers. Additionally, the principles of Total Quality Management have been implemented through the application of standard operating procedures (SOPs), employee training, and regular service evaluations. High-quality service has a positive impact on customer satisfaction, as customers feel comfortable and satisfied with the service provided.

However, this study also identified several challenges, particularly during peak hours when customer queues increase, leading to slower service. Therefore, the company needs to enhance the effectiveness of HR management and service delivery to ensure service quality remains consistent under various conditions. Thus, it can be concluded that the quality of human resources is closely linked to customer satisfaction, and the implementation of Total Quality Management can help the company improve service quality sustainably.

Gap Analysis of Digital Tracebeality Implementation in Small-Scale Halal Industries

2026-06-26T18:48:04+07:00

The rapid growth of the halal industry in Indonesia has increased the demand for more transparent, accountable, and integrated product management systems. One technological innovation that supports this need is digital traceability, a technology-based tracking system that enables stakeholders to access information related to the origin of raw materials, production processes, storage, and product distribution. Through digital traceability, consumers can verify the halal integrity of products more easily, thereby enhancing trust and confidence in halal-certified products. Despite its potential benefits, the implementation of digital traceability among small-scale halal enterprises remains limited and faces several significant challenges.

This study aims to analyze the implementation gap of digital traceability systems among halal Micro, Small, and Medium Enterprises (MSMEs) using a qualitative research approach. Data were collected through direct interviews with owners and managers of small-scale halal food businesses located in Ujungnegoro. The findings indicate that most halal MSMEs have not yet adopted digital traceability systems effectively. Several factors contribute to this situation, including limited digital literacy among business owners, high implementation and maintenance costs, inadequate technological infrastructure, and a lack of technical guidance and training from relevant institutions.

Furthermore, the study reveals a substantial gap between increasing consumer expectations for halal transparency and the readiness of MSMEs to adopt digital technologies. While consumers are becoming more concerned about product authenticity and traceability, many MSMEs still rely on conventional record-keeping methods. Therefore, a gradual implementation strategy, supported by government policies, training programs, technological assistance, and collaboration among stakeholders, is essential to facilitate digital transformation and improve the competitiveness and sustainability of the small-scale halal industry in Indonesia.

Design of a Multi-Platform Motorcycle Workshop E-Commerce and Reservation System with Payment Gateway Integration

2026-06-26T18:44:19+07:00

The development of digital technology has encouraged motorcycle workshop SMEs to improve their service and transaction processes through integrated information systems. However, many workshops still use manual procedures for service reservations, spare part sales, inventory recording, and payment management, which can cause service delays and data recording errors. This study aims to design and implement a multi-platform motorcycle workshop e-commerce and reservation system integrated with a payment gateway. The system was developed using the Waterfall method, with Flutter for the customer mobile application, Laravel for the web-based administrator system, and MySQL as the database. The system provides features for spare part catalogs, service booking, digital payment, transaction tracking, product management, inventory monitoring, and sales reports. Testing results showed that the system achieved a 96% success rate in Black Box Testing and an average User Acceptance Test score of 4.31 out of 5. Overall, the system can improve service efficiency and support the digitalization of motorcycle workshop operations.

Development of an Internet of Things-Based Animal Trap Using Motion Sensors and Camera: A Case Study on Rat Traps

2026-06-26T21:03:58+07:00

This research aims to develop and implement an Internet of Things (IoT)-based rat trap utilizing dual infrared motion sensors and an ESP32-CAM module. The system integrates infrared sensors calibrated as automated triggers for a servo-driven hatch door, with the ESP32-CAM providing remote visual validation, managed by a NodeMCU ESP8266 as the primary microcontroller. Telemetry data, real-time push notifications, and manual overrides are synchronized seamlessly through the Firebase Realtime Database and a custom Android application. Evaluated via black-box testing using white lab rats as test objects, the empirical results demonstrate that all integrated components operate effectively. The conditional automation sequence, including the 5-minute sensor masking and timeout safeguards, executed successfully without system failure. Furthermore, system integration proved highly stable, exhibiting an average control and data telemetry latency of 1–3 seconds, while mobile push notifications were delivered in less than 3 seconds. These findings confirm that the multi-sensor and camera infrastructure can be utilized optimally to deliver a responsive, reliable, and modern pest control solution.

Water Level Prediction Based on Internet of Things and Machine Learning

2026-06-27T22:42:41+07:00

This study develops an Internet of Things and machine learning based water-level prediction system for Jalan Embun Suryana, Samarinda. The device uses an ESP32, a JSN-SR04T ultrasonic sensor to measure water-surface distance, and a tipping-bucket ombrometer for rainfall. Data are synchronized to Firebase and transformed into a simple accumulated-rainfall feature to train a simple linear regression model. The research stages comprised Requirements Analysis, System Design, Sensor Data Collection, Model Training, Model Testing, Device Testing, and Integration. An Android application provides real-time visualization and early-warning notifications. Experiments indicate good sensor accuracy (JSN-SR04T; tipping-bucket 0.7 mm/tip) and model performance (MSE = 0.562 cm², RMSE = 0.749 cm, R2 = 0.992). In sum, data acquisition, prediction, and notifications operated as designed, and the simple linear regression produced water-level forecasts. At the study site, the association between increasing rainfall and rising water level was corroborated by repeated manual field measurements.

Design and Construction of a Web-Based Employee E-Recruitment Information System at PT. Digdaya Solusi Teknologi Jakarta

2026-06-27T22:48:15+07:00

In the era of digital transformation, an automated manpower procurement system is a crucial factor in strengthening the operational structure of technology companies. PT. Digdaya Solusi Teknologi (DST) Jakarta currently faces administrative obstacles due to recruitment procedures that are still conventional and paper-based. This condition triggers the risk of physical archive accumulation, slow coordination of selection status, and vulnerability to applicant data damage. This research aims to automate the flow from registration to selection management through the design and development of a web-based E-Recruitment information system. The software development methodology used is the Waterfall model. The research stages include requirements analysis, visual modeling using Unified Modeling Language (UML), program code implementation, and system testing. Data collection was carried out through in-depth interview techniques, HRD workflow observations, and documentation studies. The research results show that this digital platform is capable of significantly accelerating the employee procurement process and providing an organized talent database. Functionality testing using Blackbox Testing proves the system is feasible for use, while evaluation through the System Usability Scale (SUS) confirms an increase in satisfaction and ease of interaction for both admins and prospective applicants.

Development of a Web-Based Decision Support System for Child Growth Evaluation using the Simple Additive Weighting (SAW) Method at the Narwastu Child Development Center

2026-06-26T18:54:15+07:00

Monitoring child growth is an essential activity for assessing children's health and development from an early age. The Narwastu Child Development Center (CDC) routinely measures children's weight and height every month as the basis for growth evaluation. However, the current manual or semi-digital recording and evaluation process presents several challenges, including data processing errors, difficulties in managing historical measurement records, and inefficient evaluation procedures. This study aims to develop a web-based Decision Support System (DSS) to facilitate a more effective and structured child growth evaluation process using the Simple Additive Weighting (SAW) method. The SAW method is employed to evaluate children's growth based on weighted criteria of body weight and height, producing preference values and corresponding growth status classifications. The system development process consisted of requirements analysis, system design, implementation, and testing using the Black Box Testing method. The results indicate that the developed system is capable of managing child growth data, performing automatic SAW calculations, presenting growth evaluation results, and providing historical records as well as graphical visualizations of children's growth. Based on the testing results, all system functions operated as expected and met the specified requirements. Therefore, the developed system effectively improves the efficiency of child growth data management and supports decision-making in the child growth evaluation process at the Narwastu Child Development Center.

Designing Simple Performance Indicators for System Evaluation Digital Traceability in the Halal Industry (Case Study: Raja Kebab MSMEs Certified Halal, Pekalongan)

2026-06-27T22:38:32+07:00

The mandatory halal certification policy in Indonesia accelerates MSME integration into the global halal ecosystem, though often viewed merely as an administrative hurdle. This study evaluates the digital traceability system of halal-certified MSMEs, using Raja Kebab in Pekalongan as a case study. Employing a mixed-methods approach and GAP analysis, data was collected via observation and questionnaires from owners and consumers. Findings reveal a critical operational gap: despite high consumer confidence, MSMEs lack fundamental traceability documentation. Production documentation showed the most severe gap (GAP 2.67), lacking SOPs and batch coding, relying entirely on the owner's memory. Distribution traceability showed a moderate gap (GAP 1.33) due to missing halal logos on packaging. The study recommends transitioning from memory-based operations to simple, cost-free digital traceability tools like Google Forms, Sheets, and POS applications. This framework offers a scalable, low-cost solution for micro-enterprises to maintain halal integrity and consumer trust.

IoT Based Temperature and Humidity Monitoring System for Black Soldier Fly (BSF) Biopond Using ESP32

2026-06-27T22:45:19+07:00

Black Soldier Fly (BSF) maggot cultivation requires stable environmental conditions, particularly temperature and humidity, to ensure optimal growth and productivity. However, many small-scale breeders still rely on manual monitoring, resulting in inefficient environmental control and increased risk of unfavorable cultivation conditions. This study proposes an Internet of Things (IoT)-based monitoring and control system for BSF bioponds using an ESP32 microcontroller, DHT22 temperature and humidity sensor, soil moisture sensor, DC exhaust fan, L298N motor driver, and Firebase as a real-time monitoring platform. The system was developed using the Waterfall methodology, including requirements analysis, system design, implementation, testing, and evaluation. Environmental data collected by the sensors are transmitted to Firebase for remote monitoring, while the exhaust fan is automatically activated to stabilize environmental conditions when the measured values exceed predetermined thresholds. Experimental results demonstrate that the proposed system successfully performs real-time monitoring and automatic environmental control with stable communication between hardware and the cloud platform. The integration of IoT technology with automatic temperature regulation improves monitoring efficiency, minimizes manual intervention, and provides a practical solution for household-scale BSF cultivation. The developed system is expected to support sustainable maggot farming by maintaining optimal environmental conditions and improving cultivation productivity.

Design and Development of an Automatic Clothes Folding Machine with Garment Fragrance and Heating Features Based on Arduino Uno

2026-06-28T22:40:32+07:00

Manual clothes folding requires considerable time and effort, making it necessary to develop a more efficient and practical solution for everyday life. This study aims to design and build automatic clothes folding device based on Arduino Uno, equipped with heating and fragrance features to improve the quality of folding results. The device design encompasses the integration of mechanical and electronic components controlled via three control buttons, making operation simple and efficient. Testing was conducted on three types of clothing, namely t-shirts, shirts, and jerseys, considering material variations such as cotton, polyester, and canvas. The test results show that the device can fold clothes in an average time of 6.67 seconds, faster than the manual method which requires an average of 7.05 seconds. The heating and fragrance feature also proved to function well, producing neatly folded clothes with a fresh scent. The system operates stably, although folding performance is influenced by the type of fabric used. This device provides added value in the clothing packaging process and has the potential to become a practical solution that enhances efficiency and comfort in everyday clothes folding activities.

MySQL vs MongoDB on Small-Scale E-Commerce Query Workloads: A Systematic Review of Database Performance Optimization

2026-06-28T22:44:29+07:00

The rapid growth of small-scale e-commerce platforms in Indonesia has increased the need to select an appropriate database management system (DBMS) that ensures performance, scalability, and data integrity. This study compares the performance, flexibility, and scalability of MySQL and MongoDB through a Systematic Literature Review (SLR) of 20 scientific articles published between 2021 and 2025. The results show that no database is absolutely superior, since the optimal performance of each system depends on the workload it handles. MongoDB performs better in write-intensive operations, simple data reads, schema flexibility, and horizontal scalability, making it suitable for managing dynamic data such as product catalogs, user sessions, and shopping carts. MySQL is more reliable in maintaining transactional integrity through ACID properties, executing complex relational queries efficiently, and ensuring data reliability, making it the preferred choice for critical data such as financial transactions and inventory management. Based on these findings, this study recommends a Polyglot Persistence strategy, a hybrid approach in which MySQL serves as the source of truth for critical transactional data while MongoDB manages dynamic data requiring high performance and flexibility. This strategy offers evidence-based guidance for developers and system architects building efficient, responsive, and consistent small-scale e-commerce platforms.

Implementation of Multi-Factor Authentication and One-Time Password for Debian Remote Server Administration

2026-06-29T13:31:00+07:00

Server security is an important aspect of information technology infrastructure management because servers function as data storage centers and providers of network services. In practice, server administration is commonly performed remotely through the Secure Shell (SSH) protocol. Although SSH provides encrypted communication, authentication based solely on usernames and passwords still has several weaknesses, such as vulnerability to brute force attacks, credential theft, phishing, and unauthorized access. Therefore, an additional security mechanism is needed to strengthen server administrator access protection.

This study aims to implement Multi Factor Authentication (MFA) and One Time Password (OTP) for Debian-based remote server administration. The research methods include problem identification, literature review, system design, implementation, testing, and evaluation. The implementation was carried out by integrating Google Authenticator with SSH services through the Pluggable Authentication Module (PAM).

The results indicate that MFA and OTP successfully add an extra layer of security to the administrator authentication process. In addition to entering a username and password, users are required to provide a unique OTP code that is valid only for a limited period. Testing results show that the system is capable of rejecting login attempts that fail to meet one of the authentication factors. Therefore, the implementation of MFA and OTP is effective in reducing the risk of unauthorized access caused by password leakage and improving the security of Debian remote server administration.

Implementing Infrastructure as Code Using Ansible on Debian Server Administration

2026-06-29T20:33:53+07:00

The growth of cloud computing and virtualization has changed how IT infrastructure is managed, moving from doing things by hand to using Infrastructure as Code (IaC) methods. This study uses Ansible as a tool for managing server setup to automate tasks on Debian 12 servers within a virtualized environment. The setup includes four main areas of administration:managing users and masing SSH more secure, setting up a firewall using UFW,insatlling the Ngnix web server with Jinja2 templates, and setting up automatic security uodates through unattended upgrades. The method used is Design Science Research (DSR), which involves an experimental approach that uses two connected virtual machines. The results show that Ansible was able to fully aautomate the setup process, taking a total of 54.121. seconds to complete. It also demonstrated that all the roles worked correctly even when run more than once, with no changes needed on re-run. This setup helps cut down on mistakes people might make, makes sure settings are the same every time, and allows for creating the same infrastructure again and again.

Implementation of the AES-256 Algorithm for Securing Data Transfer on a Debian Server

2026-06-30T07:25:20+07:00

File Transfer Protocol (FTP) has a weakness because the data sent is not encrypted, making it vulnerable to attacks. This study aims to implement AES-256 encryption on a Debian-based FTP service using FTPS via VSFTPD and OpenSSL. The research stages include serv-er installation and configuration, SSL/TLS certificate creation, FTP account creation, and connection testing using WinSCP. The results show that FTPS was successfully implemented on a Debian server with IP 192.168.10.2. File transfer testing ran well using TLS 1.3 and the TLS_AES_256_GCM_SHA384 cipher so that data is more secure during the transmission process. This implementation has been proven to improve data transfer security by ensuring the confidentiality and integrity of information.

Design and Construction of Hydroponic Plant Cultivation Based on the Internet of Things on Lettuce

2026-06-30T07:38:30+07:00

This research discusses the design of an Internet of Things (IoT)-based hydroponic cultivation system applied to lettuce plants. This system is designed to automatically monitor and control plant environmental conditions, such as solution pH, nutrient content (TDS), and water temperature, using an ESP32 microcontroller connected to related sensors. Data obtained from the sensors are then sent to a Firebase-based platform for real-time monitoring via a monitoring website. This system is also equipped with automatic water pump control to increase or decrease the nutrient solution according to the conditions detected by the sensors. With this system, users can monitor and manage hydroponic plants without having to be physically present at the location, simply by accessing the website to see the condition of the plants directly. The results of this study indicate that the system is able to increase lettuce plant productivity through more optimal growth and better quality. In addition, this system also provides efficiency in cultivation management, both in terms of time, energy, and nutrient use, because the monitoring and control processes are carried out automatically and in real-time.

Identifying Halal Critical Points in the Supply Chain of the Halmahera Hydroponic Home Industry

2026-06-25T14:45:56+07:00

The increasing number of Muslim consumers who are concerned about the halal status of food products has made halal assurance essential, not merely at the level of the finished product but across the entire supply chain. This study sets out to identify Halal Critical Points (HCP) within the supply chain of a hydroponic home-based enterprise, namely Halmahera Home Industry, situated in Kajen District, Pekalongan Regency, Central Java. A descriptive qualitative approach was applied, drawing on field observation and in-depth interviews with the business operator. Eight HCP aspects, namely nutrients and fertilizer, growing media, water source, harvesting tools, cleanliness of the production area, packaging, storage, and distribution, were examined through the lens of the Halal Supply Chain Management (HSCM) framework. The results indicate that all eight HCP aspects within this home-based enterprise have, in principle, been satisfied, despite the absence of formal halal certification. These findings point to the considerable potential of small-scale hydroponic businesses for integration into a wider halal food supply chain, on condition that formal certification is subsequently pursued. This study contributes to the development of a more inclusive halal audit framework intended for micro and small enterprises operating within the urban agriculture sector.

Design of Physical Security System for Server Room Using Esp32-Cam and Ultrasonic Sensor with Automatic Alarm when Motion Detection is Detected (Case Study: Diskominfo Langkat District)

2026-06-30T11:15:04+07:00

Penerapan Sistem Pemerintahan Berbasis Elektronik (SPBE) yang semakin meningkat membutuhkan keamanan fisik yang andal untuk ruang server sebagai pusat penyimpanan dan pengolahan data. Namun, keamanan ruang server di Dinas Komunikasi dan Informatika (DISKOMINFO) Kabupaten Langkat masih dipantau secara manual dan belum memiliki sistem deteksi otomatis. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem keamanan fisik ruang server menggunakan ESP32-CAM, sensor ultrasonik HC-SR04, sensor suhu DHT11, dan alarm buzzer otomatis. Metode penelitian yang digunakan adalah Penelitian dan Pengembangan (R&D), termasuk desain sistem, implementasi perangkat keras dan perangkat lunak, serta pengujian sistem. Sistem yang dikembangkan mampu mendeteksi pergerakan objek dalam rentang 5–200 cm, mengaktifkan alarm secara otomatis, menampilkan pemantauan visual secara real-time melalui ESP32-CAM, dan memantau suhu ruang server. Hasil pengujian menunjukkan bahwa sensor ultrasonik berhasil mendeteksi objek dalam rentang yang ditentukan, buzzer berfungsi dengan baik sebagai alarm, ESP32-CAM menyediakan pemantauan real-time melalui browser web, dan sensor DHT11 secara efektif memantau suhu ruangan. Oleh karena itu, sistem ini dapat mendukung keamanan ruang server fisik dan meningkatkan efektivitas pemantauan di DISKOMINFO Kabupaten Langkat.

Development of a Business Intelligence Dashboard for Performance Analysis of TheLook E-Commerce Based on a Data Warehouse

2026-06-30T12:45:02+07:00

As digital transactions in e-commerce continue to grow, organizations require data processing systems capable of transforming large volumes of operational data into strategic information. This study aims to implement a Business Intelligence (BI) dashboard to support business analysis in TheLook E-Commerce. The study applies an Extract, Transform, Load (ETL) process, data warehouse development, data mart construction, and interactive data visualization. TheLook E-Commerce dataset consists of customer, product, order item, inventory, distribution center, and user activity data. The research process includes importing data into PostgreSQL, storing data in a staging area, developing a data warehouse using a star schema, creating a data mart, and visualizing data through a Tableau dashboard. The data warehouse consists of one fact table, fact_sales, and several dimension tables, including dim_customer, dim_product, dim_orders, dim_distribution_center, and dim_date. The implemented dashboard provides key business indicators such as revenue, profit, total orders, total customers, average order value, gross margin, product performance, order status distribution, customer segmentation, and sales trends. The results indicate that TheLook E-Commerce experienced growth in sales and profit margins. However, challenges remain in maintaining order volume and customer loyalty. Overall, the data warehouse-based BI implementation supports more effective business operations and decision-making processes.

Comparison of Xgboost, Lstm, and Neural Prophet Models for Red Cayenne Pepper Price Prediction in East Java

2026-06-30T13:41:37+07:00

The price of red cayenne pepper in Indonesia, particularly in East Java Province, frequently experiences significant fluctuations, affecting food inflation and economic stability. Accurate price forecasting is therefore essential to support decision-making in food supply chain management and price stabilization policies. This study compares the forecasting performance of three models, namely XGBoost, LSTM, and Neural Prophet, using historical price data from September 2024 to August 2025 obtained from the official Siskaperbapo website of the East Java Provincial Department of Industry and Trade (DISPERINDAG). A quantitative time series forecasting approach was employed, and model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results indicate that XGBoost achieved the highest prediction accuracy, with an RMSE of 0.52, MAE of 0.40, and MAPE of 1.42%, outperforming LSTM and Neural Prophet. The findings also show that model selection, dataset length, and training-test split proportion significantly influence forecasting performance, with longer datasets and larger training sets generally improving prediction accuracy. Overall, XGBoost proved to be the most accurate and stable model for forecasting red cayenne pepper prices, providing valuable support for AI-based food price prediction and agricultural policy decision-making.

UI/UX Design and Frontend Implementation in the Redesign of the National Archives Information Network Website

2026-06-28T19:23:47+07:00

The National Archival Information Network (JIKN) serves as a digital platform that provides public access to archival information. However, the existing website interface showed several usability and visual design limitations that affected user experience and accessibility. This study aimed to redesign the JIKN website by applying User Interface (UI) and User Experience (UX) principles and implementing the redesigned interface into a functional frontend web application. The research approach involved problem identification, user needs analysis, wireframe design, prototype development, user testing, and frontend implementation. The redesigned interface was developed using PHP and Tailwind CSS to ensure responsiveness, consistency, and adaptability across various devices. The results demonstrated that the redesigned website offers a more modern visual appearance, improved navigation structure, and enhanced usability compared to the previous version. User testing indicated that the new interface increased user satisfaction and facilitated easier access to archival information services. Furthermore, the frontend implementation successfully transformed the design prototypes into a functional and responsive web interface while maintaining design consistency. In conclusion, the redesign and frontend implementation of the JIKN website improve accessibility, usability, and the overall quality of digital archival information services, thereby supporting a more effective user experience in accessing archival resources.

Research Trends in Mobile Payment and E-Wallet Adoption: A Bibliometric Study Based on Dimensions and VOSviewer

2026-06-29T17:39:56+07:00

The rapid growth of digital payment technologies has significantly increased the adoption of mobile payment and e-wallet services worldwide. Consequently, research on digital payment adoption has expanded considerably in recent years. This study aims to map research trends related to mobile payment and e-wallet adoption during the 2023–2026 period using a bibliometric approach. Data were collected from the Dimensions database using predefined search criteria, resulting in 446 publications for analysis. Bibliometric mapping was conducted using VOSviewer to identify research clusters, emerging topics, country collaborations, and influential publications. The results revealed three dominant research themes, namely Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Financial Inclusion. Overlay visualization indicated a shift in research focus from traditional technology acceptance factors toward financial inclusion, mobile money, and policymaking perspectives. Furthermore, citation analysis identified several influential studies that have shaped recent developments in the field. The findings provide a comprehensive overview of the current research landscape and highlight potential directions for future studies on digital payment adoption.

Implementation of Automatic Mobile Application for Ordering Flower Arrangements and Partner History using Android-Based Human-Centered Design (Case Study: Rensya Florist Cinta)

2026-06-30T21:14:58+07:00

Toko Cinta Rensya Florist is a service provider specializing in flower arrangements that aims to improve service efficiency, as it currently does not rely on a formal system. Orders are still placed through phone calls and WhatsApp, transaction records still rely on downloaded bank statements, and partner history has not been recorded, making it time-consuming to retrieve partner information for future orders. This research aims to design a more organized Android-based system for order processing, transaction recording, and partner history management by applying the Human-Centered Design (HCD) approach, which consists of four stages: understanding the user context, specifying user requirements, producing a design solution (mockup), and evaluating the design. Testing was conducted through a System Usability Scale (SUS) survey administered to the business owner, staff, and prospective users. The test results show an average score of 73 with a grade scale of “B” in the “Good” category, indicating that the application is acceptable and can be used effectively by users. It is concluded that applying the HCD method produces a flower-arrangement ordering system that is effective, efficient, and well suited to the needs of Toko Cinta Rensya Florist users.

System Requirements Analysis for the Implementation of Digital Traceability in the Sme-Scale Eid Al-Fitr Cookie Industry

2026-06-03T10:04:54+07:00

Small, and Medium Enterprises (UMKM) in the dry Eid cake production sector face primary challenges related to supply chain transparency and product quality assurance. The lack of organized record-keeping systems makes product traceability difficult to implement, especially when issues arise such as customer complaints or product recalls. This research aims to identify and analyze information system needs for implementing digital traceability in these SMEs. The approach used is qualitative, with system needs analysis techniques based on the early phases of SDLC (Software Development Life Cycle), including direct field observations, semi-structured interviews, and process document reviews. Findings show that the AS-IS condition in most SMEs is characterized by the absence of digital documentation, lack of product batch codes, and no backward tracing mechanisms. From these gaps, this research develops a TO-BE condition using affordable technologies such as Google Form, QR Code, and Google Sheets as the basis for a simple traceability system suitable for SMEs. Comparison between conditions before and after implementation indicates significant improvements in accountability, food safety, and consumer trust. This research is expected to provide practical benefits for SME actors while enriching food information system studies in Indonesia.

Application of the K-Nearest Neighbors Algorithm in Anime Recommendation System Based on Genre Preferences and Web-Based Ratings

2026-06-17T16:31:50+07:00

The rapid growth of the anime industry has resulted in an increasing number of anime titles being released every year. The large variety of available anime often makes it difficult for users to find anime that match their interests and preferences. The process of searching for anime manually requires considerable time, as users need to review genres and ratings individually. Therefore, a recommendation system is needed to assist users in finding suitable anime more quickly and efficiently. This study aims to develop a web-based anime recommendation system using the KNearest Neighbors (KNN) algorithm based on users' genre and rating preferences. The dataset used in this research was obtained from the Anime Recommendations Database available on Kaggle, consisting of 12,294 anime records. The research process includes data collection, data cleaning, attribute selection, data transformation, KNN algorithm implementation, and web-based system development. The KNN algorithm is applied to calculate the similarity between user preferences and anime data using the Euclidean Distance method. The results of this study indicate that the developed system is capable of providing anime recommendations that match users' preferences based on selected genres and ratings. The system also provides features for anime data management, recommendation searches, anime detail views, and a watchlist feature for saving anime that users intend to watch. The implementation of this system helps users find suitable anime more easily, quickly, and efficiently compared to manual searching methods.

Report on Teaching Assistance Activities at SDN 060883 Medan Petisah for the 2025/2026 School Year

2026-07-01T23:10:27+07:00

The Teaching Assistance Program is an implementation of the Independent Learning–Independent Campus (MBKM) policy, aimed at providing practical experience for education students. This article is compiled based on the implementation of teaching assistance activities at UPT SDN 060883 Medan Petisah during the 2026/2027 academic year. The study focuses on four main pillars: academic, technology adaptation, non-academic, and school administration. The methods used in implementing the program include classroom observation, collaborative learning, and evaluation of student learning outcomes. The results of the program showed a significant improvement in students' teaching readiness, adaptation to digital tools in elementary school learning, and positive contributions to managing non-academic aspects such as extracurricular programs and school cleanliness. Through self-reflection, the program successfully honed future educators' critical thinking, communication, and class management skills.

Performance Analysis of Nginx and Apache2 Web Servers on Debian Using Stress Testing Methods

2026-07-02T00:02:09+07:00

Web servers play a crucial role in supporting web-based services by receiving and processing user requests. The performance of a web server is one of the key factors that determines service quality, especially when the server is required to handle a large number of concurrent users. Therefore, selecting an appropriate web server is essential to ensure optimal system performance.

This study aims to compare the performance of two widely used web servers, Nginx and Apache2, running on the Debian operating system using a stress testing approach. The testing process was conducted by generating simultaneous access loads using several benchmarking tools, including Apache Benchmark (ab), Autocannon, and JMeter. The performance parameters evaluated in this study include throughput, latency, requests per second (RPS), response time, and server resource utilization such as CPU and memory usage.

The results show that Nginx is able to handle a large number of concurrent connections more effectively than Apache2. This is indicated by its lower latency values and higher requests-per-second performance. Meanwhile, Apache2 demonstrates relatively stable throughput across various testing scenarios. Based on the findings, Nginx is recommended for environments with high traffic demands and resource efficiency requirements. On the other hand, Apache2 remains a suitable choice for applications that require greater configuration flexibility and extensive module support.

Implementation of the Apriori Algorithm for User Access Pattern Analysis on Debian Web Server

2026-07-01T23:57:16+07:00

The increasing number of website user activities generates large amounts of log data that are often underutilized as a source of information for analyzing user access patterns. This study aims to implement the Apriori algorit hm to analyze user access patterns based on Apache Access Log data collected from a website running on Debian 12 using Apache Web Server and WordPress. The research data were obtained from the access.log file and processed through several preprocessing stages, including filtering, cleaning, and transforming the log data into transaction datasets based on user sessions. The Apriori algorithm was then applied to generate frequent itemsets and association rules using predefined minimum support and confidence values. The results show that the Apriori algorithm successfully identifies relationships among web pages that are frequently accessed together by users. The discovered patterns provide valuable insights into user navigation behavior, enabling website administrators to optimize website structure, improve service quality, and support data-driven decision-making. Therefore, the implementation of the Apriori algorithm on Apache Access Log data can serve as an effective approach for analyzing user behavior based on web server log data.

Usability Engineering Approach for Simantep Unimed Learning Management System Optimization Using User-Centered Design

2026-06-24T16:03:33+07:00

SIMANTEP UNIMED is a web-based Learning Management System used to support academic and administrative activities within the Faculty of Mathematics and Natural Sciences, Universitas Negeri Medan. This study aimed to improve the user interface and user experience of the system by identifying user needs and implementing a gallery feature through a User-Centered Design (UCD) approach. The research employed interviews, observations, usability testing, Think-Aloud sessions, and Analysis of Verbal Behavior to evaluate the existing system and the proposed design prototype. A prototype was developed in Figma and assessed using usability attributes, including effectiveness, efficiency, and satisfaction. The results demonstrated substantive improvements in usability. The redesigned prototype achieved effectiveness and efficiency scores of 100%, while the satisfaction score reached 89.5 on the System Usability Scale. Verbal behavior analysis recorded 24 user comments, consisting of 58.3% positive, 29.2% neutral, and 12.5% negative comments. Compared with the initial evaluation, positive comments increased by 40.1%, while neutral and negative comments decreased by 13.2% and 26.9%, respectively. These findings indicate that the User-Centered Design approach successfully addressed usability issues and enhanced the overall user experience of SIMANTEP UNIMED. The proposed design can serve as a reference for future system development.

Comparative Performance Analysis of Apache2 and Nginx Web Servers on Ubuntu Server Using the Apache Benchmark Method

2026-07-02T00:05:06+07:00

Advancements in information technology have led to an increasing demand for fast, stable, and efficient website services. Web server performance is a crucial factor in maintaining service quality, particularly when handling simultaneous user requests. Apache2 and Nginx are two widely used web servers on Linux operating systems; they possess distinct architectural characteristics that result in differing performance levels. This study aims to analyze and compare the performance of Apache2 and Nginx web servers on Ubuntu Server using the Apache Benchmark method. The research employed an experimental approach involving the installation, configuration, and testing of both web servers within a VirtualBox virtualization environment. The performance metrics evaluated included requests per second, time per request, failed requests, and transfer rate. Testing was conducted across three scenarios featuring varying numbers of requests and concurrent users. The results indicate that both web servers successfully handled all requests without any failures. However, Nginx demonstrated superior performance compared to Apache2, achieving higher requests per second and transfer rates, as well as lower time per request. This study provides comparative performance data for Apache2 and Nginx on Ubuntu Server, serving as a reference for selecting the appropriate web server based on specific implementation requirements.

Design and Development of a Website-Based Donation System for Usable Goods in Gama Pinang Urban Village (RW 03 Area) Using the Prototype Method

2026-06-30T21:16:13+07:00

The donation of usable goods in RW 03 of Gama Pinang Village is still managed manually, which causes several problems such as difficulties in managing donation data, monitoring the availability of donated goods, managing donation information, and organizing the distribution process. This study aims to design and develop a website-based usable goods donation system for RW 03 so that the donation, data recording, and distribution processes can run more effectively and efficiently. The system development method used is the prototype method, which allows direct interaction between the developer and the user during the system refinement process. Data were collected through observation, interviews, and literature studies to obtain system requirements suited to the conditions of RW 03. The system was built using the Laravel framework, the PHP programming language, the MySQL database, and Bootstrap for the user interface. The results show that the developed system is able to integrate the management of donor data, donated goods data, recipient data, distribution data, as well as donation and distribution reports. The system also facilitates monitoring of the status of donated goods and the distribution process to recipients in need, making the donation process more efficient, transparent, and structured in supporting the social activities of the community in RW 03 of Gama Pinang Village.