https://ioinformatic.org/index.php/JODS/issue/feedJournal of Optimization and Decision Science (JODS)2026-04-23T13:53:47+07:00Dr. Ir. Akim Manaor Hara Pardede, ST., M.Komakimmhp@ioinformatic.orgOpen Journal Systems<p><strong>The Journal of Optimization and Decision Science (JODS)</strong> is a scientific journal focused on the development and application of optimization models, computational algorithms, and intelligent decision-making methods in computer science and informatics. The journal aims to provide a dedicated platform for researchers, academics, practitioners, and students to disseminate high-quality research addressing complex computational problems through rigorous optimization techniques and data-driven decision-making approaches.</p> <p><strong>JODS</strong> is published by the Kita Tulis Foundation and is published three times a year (April, August, and December).</p> <p>All submitted articles undergo a double-blind peer-review process and are evaluated based on their scientific contribution, originality, methodological soundness, and relevance to optimization and decision science in computing.</p> <p>The journal's scope includes (but is not limited to):<br />Computational Optimization and Algorithm Development<br />Metaheuristics and Heuristic Algorithms<br />Decision Support Systems and Intelligent Decision Models<br />Machine Learning and Artificial Intelligence for Optimization<br />Resource Allocation and Scheduling in Computing Systems<br />Optimization in Cloud, Edge, and Distributed Computing<br />Network Optimization and System Performance Analysis<br />Data-Driven Decision Making and Predictive Modeling<br />Simulation and Modeling of Complex Systems<br />Intelligent and Smart Computing Systems</p> <p><strong>JODS</strong> is committed to being a focused and reputable reference in the field of optimization and decision science, particularly in advancing computational methods and intelligent systems to solve real-world problems at both the national and international levels.</p>https://ioinformatic.org/index.php/JODS/article/view/2270Controlling Consumable Material Inventory Using the Multi-Item EOQ Method in the Spare Parts Warehouse of PT. XYZ2026-04-22T16:29:25+07:00Fahrul Raharja22032010150@student.upnjatim.ac.idEnny Aryanny22032010150@student.upnjatim.ac.id<p>Inventory management plays a critical role in maintaining effective maintenance operations within manufacturing environments, where machine reliability significantly influences production continuity. This internship report examines the application of the Multi-Item Economic Order Quantity (EOQ) model to optimize consumable inventory in the Engineering Division of PT. XYZ, a leading ceramic manufacturing company in Indonesia. The division oversees a diverse range of technical consumables, such as mechanical components, metal materials, fasteners, electrical equipment, and transmission parts, all characterized by variable demand and extended procurement lead times.</p> <p>Suboptimal ordering practices may result in stock shortages that disrupt maintenance activities or excess inventory that increases holding costs and storage inefficiencies. The study utilizes historical procurement data, including an annual material demand of Rp378,190,765, total ordering costs of Rp1,765,530, and annual holding costs of Rp920,106, which encompass warehouse operations and storage-related expenses distributed across all item categories. The Multi-Item EOQ model is applied using a consolidated minor ordering cost parameter to estimate an optimal aggregate order value of Rp38,096,850 per cycle. In addition, optimal order quantities are determined for each item based on unit cost and yearly demand levels.</p> <p>The analysis identifies an optimal reorder interval of 37 working days, allowing for more systematic procurement scheduling that balances ordering frequency and inventory holding costs. The findings indicate that the Multi-Item EOQ approach enhances procurement efficiency, improves inventory organization, ensures the availability of critical consumables for maintenance activities, and minimizes operational risks associated with inventory imbalance. This approach provides a practical foundation for improving future procurement strategies within the engineering division.</p>2026-04-23T00:00:00+07:00Copyright (c) 2026 Journal of Optimization and Decision Science (JODS)https://ioinformatic.org/index.php/JODS/article/view/2271Decision Support System Modeling for Determining Thesis Advisors Using Profile Matching2026-04-22T16:29:01+07:00Yusrizal Hakimyusrizalhakim574@gmail.comAmirah Nafiah Zalfaamirah0701232082@uinsu.ac.idAnisa Ninda Cahyanianisa0701232074@uinsu.ac.idMuhammad Al Faris Syabilmuhammad0701233180@uinsu.ac.id<p>This study aims to develop a decision support system based on the Profile Matching method to provide automatic and objective recommendations for thesis advisors. This system is designed to address the problem of advisor selection, which was previously done manually by students, often resulting in an imbalance in the workload and a mismatch in faculty competencies. The Profile Matching method is used to compare lecturer competency profiles with student needs based on Core Factors and Secondary Factors, including research field suitability, supervisory experience, and number of students supervised. Research data was obtained from students and lecturers in the Computer Science study program, then tested manually to evaluate the accuracy of the system's recommendations. The results showed that the system was able to generate a ranking of advisors that matched students' research fields, while distributing the advising load more evenly. This study demonstrates that the application of a decision support system with Profile Matching can improve objectivity and efficiency in determining advisors, as well as provide a basis for the development of academic recommendation systems in the future.</p>2026-04-23T00:00:00+07:00Copyright (c) 2026 Journal of Optimization and Decision Science (JODS)https://ioinformatic.org/index.php/JODS/article/view/2272Designing a Mobile Application Prototype for Accessing and Publishing Student Journals using Figma2026-04-22T16:28:38+07:00Muhammad Aldatonang875@gmail.comArdi Ari Kurniawantonang875@gmail.comVidya Ramadhanitonang875@gmail.comMuhammad Revandy Anandatonang875@gmail.com<p>The development of digital technology has driven significant changes in academic information management, including student journal publications. This study aims to design a mobile application prototype as a medium for accessing and publishing journals within the Faculty of Science and Technology at the State Islamic University of North Sumatra. The method used is the prototyping model, which emphasizes an iterative process between designers and users. The research stages include needs identification, initial design creation, evaluation, and design refinement using Figma as an interface design tool. The results of this research are a prototype application that displays key features such as journal uploading, searching, and downloading of student scientific papers. Initial evaluations show that this design is easy to understand, has clear navigation, and meets user needs. This prototype is expected to be the basis for the development of a more integrated student journal repository system in the future.</p>2026-04-23T00:00:00+07:00Copyright (c) 2026 Journal of Optimization and Decision Science (JODS)https://ioinformatic.org/index.php/JODS/article/view/2273Identification of Batak Ethnic Groups Based on Facial Features Using Convolutional Neural Network Algorithm2026-04-22T16:28:05+07:00Muhammad Abid Syujamhdabidsyuja@gmail.com<p>Facial-based ethnic identification is a challenging task in computer vision due to subtle inter-class variations and high intra-class similarities. The Batak ethnic group in Indonesia exhibits distinctive facial characteristics that can be analyzed using deep learning approaches. This study aims to identify Batak and Non-Batak ethnic groups based on facial features using a Convolutional Neural Network (CNN) and a pretrained ResNet50 model. The research process includes image preprocessing, feature extraction, model training, and performance evaluation. Model performance is assessed using accuracy and confusion matrix analysis. Experimental results show that the CNN model achieved an accuracy of 67%, outperforming the ResNet50 model which obtained an accuracy of 50%. These findings indicate that simpler CNN architectures can be more effective than deep pretrained models when applied to limited and locally collected facial datasets.</p>2026-04-23T00:00:00+07:00Copyright (c) 2026 Journal of Optimization and Decision Science (JODS)https://ioinformatic.org/index.php/JODS/article/view/2275Design of a Web-Based Divorce Process Information System at the Banyuwangi Religious Court2026-04-22T16:26:17+07:00A. Hamdanihilmiatussoleha21@gmail.comSayyida Syarifah Fitrihilmiatussoleha21@gmail.comAsro Hidayatihilmiatussoleha21@gmail.comHilmiatus Soleha hilmiatussoleha21@gmail.com<p>The Religious Court is a government agency that handles and resolves Islamic legal matters, including divorce. The divorce process at the Banyuwangi Regency Religious Court still relies on manual services, particularly for case registration, court schedule management, and divorce certificate status monitoring. Therefore, a web-based divorce process information system is needed at the Banyuwangi Regency Religious Court. The design methods included observation, interviews, and literature review to identify system requirements. The design process involved system analysis, flowchart creation, DFD (Data Flow Diagram), ERD (Elementary Directorate Level Diagram), database design, and interface design using Figma. The results of this study are an information system design that supports structured case data management, increases information transparency for the public, and assists officers in expediting the divorce administration process. This design is expected to improve services at the Banyuwangi Religious Court and make them more accessible to the public.</p>2026-04-23T00:00:00+07:00Copyright (c) 2026 Journal of Optimization and Decision Science (JODS)