PENGELOMPOKAN WILAYAH INDONESIA BERDASARKAN KOMPONEN INDEKS PEMBANGUNAN MANUSIA DENGAN PENDEKATAN ALGORITMA K-MEANS CLUSTERING - Saputra, Nur Rukhan; Muflih, Ghufron Zaida (SKANIKA: Sistem Komputer dan Teknik Informatika, 2025)
The Human Development Index (HDI) plays a crucial role in measuring the well-being of a region's population, offering a comprehensive perspective through various indicators, including economic factors such as Gross Domestic Product (GDP). This study focuses on clustering regions in Indonesia based on HDI components using the K-Means Clustering method. The clustering divides the regions into three groups: low, medium, and high clusters, considering dimensions of education, health, and standard of living. The data used includes indicators such as expected years of schooling, mean years of schooling, life expectancy, average monthly income, and per capita expenditure. The Davies-Bouldin Index (DBI) is employed as an evaluation method to measure the quality of cluster separation. The study reveals that K-Means successfully categorizes the regions into three clusters with a DBI value of 1.17, reflecting good cluster separation. This clustering provides valuable insights into the distribution of human development across Indonesia and is expected to assist policymakers in devising effective strategies to improve well-being in each identified cluster.