Penerapan K-Means Clustering dengan Evaluasi V-Measure untuk Pengelompokan Wilayah Berdasarkan Data Potensi Sumber Kesejahteraan Sosial - Riza Mar'atus Sholihah; Fachrur Rozi (Universitas Islam Negeri Maulana Malik Ibrahim Malang, 2025)
Social welfare is a key dimension of national development, aimed at achieving social inclusion by enhancing livelihood resilience and systems, particularly in Mojokerto City and Regency. This study aims to group regions in Mojokerto Regency and City based on Social Welfare Resource Potential (PSKS) data using the K-Means Clustering method. This method was chosen for its efficiency in handling high-dimensional data and its ability to produce a predetermined number of structured clusters. The secondary data comprised 12 indicators of social welfare resource potential from the Central Berau of Statistics and Social Office of Mojokerto, reflecting the presence of institutions and community social conditions across 18 sub-districts. Research stages included data standardization using z-score, outlier detection, optimal cluster determination using the Elbow method, K-Means Clustering implementation, and cluster evaluation using the V-Measure. The results indicate that Mojokerto regions can be grouped into five clusters with specific characteristics: urban, border, historical, industrial, and mountainous areas. The V-Measure evaluation yielded a score of 0.756, with homogeneity at 0.859 and completeness at 0.672, indicating accurate clustering quality.