Analisis Wilayah Prioritas Pembangunan di Provinsi Jawa Timur Berdasarkan Indikator Sosial Menggunakan Metode K-Means Clustering - Asyafiiyah, Gita Rohma Utami; Widyastuti, Artika; Andriani, Friska (Arcitech: Journal of Computer Science and Artificial Intelligence, 2025)
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.