Implementation of K-Nearest Neigbors for Prediction of Motorcycle Service Waiting Times in Develop Tech

Authors

  • Dion Tri Huandito Universitas Islam Balitar
  • Sri Lestanti Universitas Islam Balitar
  • Filda Febrinita Universitas Islam Balitar

DOI:

https://doi.org/10.59934/jaiea.v4i1.627

Keywords:

K-Nearest Neighbors (KNN), service wait time, Confusion matrix, wait time prediction

Abstract

This research examines the application of the K-Nearest Neighbors (KNN) algorithm to predict wait times at Develop Tech, a workshop that offers affordable services but has longer spare parts wait times compared to official workshops. The research method used is descriptive quantitative, involving data collection and processing. In this study, the KNN algorithm is utilized to predict wait times by measuring distances between data points using and performing voting based on the K value to determine the final prediction. Testing on 100 data points demonstrated that KNN could predict wait times very accurately, achieving 100% accuracy, precision, and recall at certain K values. The data was split into 80% for training and 20% for testing, a method commonly used in machine learning research to ensure a balance between training data and validation. The results indicate that the KNN algorithm is reliable for predicting wait times with optimal performance at K values between 3 and 10. These findings support the conclusion that the KNN algorithm functions effectively in predicting wait times at the workshop.

Downloads

Download data is not yet available.

References

E. F. Rayo, A. C. P. Inaray, and B. Lule, “Capacity Strategies a Comparative Perspective in Manufacturing vs Service Industries,” J. Inform. Ekon. Bisnis, vol. 5, pp. 1445–1452, 2023, doi: 10.37034/infeb.v5i4.759.

L. Firdaus and T. Setiadi, “Perbandingan Algoritma Naive Bayes, Decision Tree, dan KNN untuk Klasifikasi Produk Populer Adidas US dengan Confusion Matrix,” J. Sist. Komput. dan Inform., vol. 5, no. 2, pp. 185–195, 2023, doi: 10.30865/json.v5i2.6124.

A. M. Irfan, “Analisis Perbandingan Metode Naïve Bayes dan K-NN dalam Penentuan Lokasi Layanan Administrasi BPJS Kesehatan di Provinsi Maluku .,” vol. 4, no. 2, 2024.

F. Liantoni, “Klasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor,” J. Ultim., vol. 7, no. 2, pp. 98–104, 2016, doi: 10.31937/ti.v7i2.356.

C. A. Rachma et al., “Implementasi algoritma k-nearest neighbor dalam penentuan klasifikasi tingkat kedalaman kemiskinan provinsi jawa timur,” 2022.

Y. Lin, H. Chen, W. Xia, F. Lin, Z. Wang, and Y. Liu, “A comprehensive survey on deep learning techniques in educational data mining,” pp. 1–21.

R. Habibi, I. G. Prahmana, I. Ambarita, and L. A. N. Kadim, “Prediction Analysis of Literacy Numeracy and Technology Adaptation Abilities of Students Who Participate in Teaching Campuses Using the KNN Algorithm”, j. of artif. intell. and eng. appl., vol. 3, no. 2, pp. 590–594, Feb. 2024.

A. Pratiwi, A. M. H. Pardede, and I. G. Prahmana, “Categorying Sugarcane Production Based On Factors Affecting Productivity With The K-Nearest Neighbor Algorithm”, j. of artif. intell. and eng. appl., vol. 3, no. 1, pp. 234–238, Oct. 2023.

E. Br Milala, “Grouping of Outstanding Students at Abdi Negara Vocational School Using the K-Nearest Neighbor Method”, j. of artif. intell. and eng. appl., vol. 3, no. 1, pp. 428–432, Oct. 2023.

E. Sulistio, “Classification For Predicting Heart Disease Using The K Nearest Neighbor Method Sylvani General Hospital Binjai City”, j. of artif. intell. and eng. appl., vol. 3, no. 1, pp. 521–528, Oct. 2023.

Downloads

Published

2024-10-15

How to Cite

Huandito, D. T., Sri Lestanti, & Filda Febrinita. (2024). Implementation of K-Nearest Neigbors for Prediction of Motorcycle Service Waiting Times in Develop Tech. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 4(1), 312–317. https://doi.org/10.59934/jaiea.v4i1.627