Penerapan K-Means Clustering Untuk Mengelompokan Tingkat Kemiskinan Di Provinsi Kalimantan Barat - Cahyo, Samsul Dwi; Wahyuni, Irmawati Tri; Maharani, Revalyna Octavia; Nurfaizi, Maulana; Saputro, Rujianto Eko; Tarwoto, T (Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), 2025)
This study aims to use the K-means clustering algorithm to categorize poverty levels in the West Kalimantan province. The data used for clustering represents poverty levels across four districts: Melawi, Kapuas Hulu, Sekadau, and Kayong Utara. The K-means clustering method is employed to group these districts based on similarities in their poverty levels. The clustering results reveal four distinct categories of poverty levels: Cluster 0 represents areas with very high poverty rates; Cluster 1 shows Melawi with a high poverty rate; Cluster 2 includes Sambas, Kapuas Hulu, and Sintang, with relatively low poverty rates; and Cluster 3 includes Landak, Sanggau, and Ketapang, with high poverty rates. The analysis reveals interesting patterns in the distribution of poverty across West Kalimantan, which can assist local governments in designing more effective policies for poverty reduction. This study makes a significant contribution to understanding poverty dynamics in West Kalimantan and provides a basis for more efficient decision-making in poverty alleviation efforts.