Optimization of BPJS Health Facility Distribution with K-Means Clustering Algorithm

Authors

  • Eko Aziz Apriadi Universitas Indonesia Mandiri, Lampung, Indonesia
  • Muawan Bisri Universitas Indonesia Mandiri, Lampung, Indonesia

Keywords:

Health facility distribution, BPJS, K-Means Clustering algorithm, Optimization, Healthcare service equity, Data mining

Abstract

This study aims to optimize the distribution of BPJS health facilities in Indonesia using the K-Means Clustering algorithm. The issue of unequal distribution of health facilities across various regions in Indonesia highlights the need for a more systematic and structured approach in planning the distribution of these facilities. The K-Means Clustering algorithm is used to group health facilities based on their location and type, with the goal of identifying uneven distribution patterns and providing recommendations for a fairer and more efficient distribution. The data used in this study includes information about health facilities, such as the type of facility, address, and geographical location. The analysis results show significant disparities in the distribution of health facilities in several provinces and major cities, and demonstrate the potential to improve the equitable distribution of health facilities through better planning. This research provides an important contribution in planning the optimal spread of BPJS health facilities to enhance healthcare service accessibility across Indonesia.

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Published

2025-02-02

How to Cite

Apriadi, E. A., & Bisri, M. . (2025). Optimization of BPJS Health Facility Distribution with K-Means Clustering Algorithm. International Journal of Technology and Computer Science, 1(1), 1–13. Retrieved from https://journal.uimandiri.ac.id/index.php/ijtcs/article/view/27

Issue

Vol. 1 No. 1 (2025): August

Section

Articles

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Copyright (c) 2025 Eko Aziz Apriadi, Muawan Bisri

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