Analisis Wilayah Prioritas Pembangunan di Provinsi Jawa Timur Berdasarkan Indikator Sosial Menggunakan Metode K-Means Clustering

Authors

  • Gita Rohma Utami Asyafiiyah Universitas Islam Majapahit, Indonesia
  • Artika Widyastuti Universitas Islam Majapahit, Indonesia
  • Friska Andriani Universitas Islam Majapahit, Indonesia

DOI:

https://doi.org/10.29240/arcitech.v5i1.13488

Keywords:

machine learning, Clustering, K-Means, Development

Abstract

Development is a key indicator of a region's progress; however, regional disparities remain a pressing issue due to uneven distribution of development. This study employs a data mining approach using the K-Means Clustering method. The objective is to classify priority development areas in East Java Province based on various social indicators, including total population, population growth rate, population density, Human Development Index (HDI), unemployment rate, and average years of schooling (AYS). Unlike previous studies, this research adopts a more comprehensive data-driven approach. The results show that K-Means successfully classifies regions into two clusters: priority and non-priority. A Silhouette Score of 0.45 indicates a fairly good level of cluster separation. Most of the regions in the priority cluster are regencies, while the non-priority cluster predominantly consists of urban areas. These findings confirm that K-Means Clustering is an effective decision-support tool for identifying priority development areas through data-based analysis.

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Published

30-06-2025

How to Cite

Asyafiiyah, G. R. U., Widyastuti, A., & Andriani, F. (2025). Analisis Wilayah Prioritas Pembangunan di Provinsi Jawa Timur Berdasarkan Indikator Sosial Menggunakan Metode K-Means Clustering . Arcitech: Journal of Computer Science and Artificial Intelligence, 5(1), 32–48. https://doi.org/10.29240/arcitech.v5i1.13488

Issue

Vol. 5 No. 1 (2025): June 2025

Section

Articles

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Copyright (c) 2025 Gita Rohma Utami Asyafiiyah, Artika Widyastuti, Friska Andriani

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