Identification of Indonesian Provinces Based on Socioeconomic Indicators in 2024 Using K-Means - Permatasari, Erika Putri; Iriani, Lathifa Aurellia; Widodo, Edy (LPPM-ITEBA, 2026)
Poverty remains a persistent development challenge in Indonesia, characterized by substantial disparities across regions. Differences in social and economic conditions among provinces highlight the need for a comprehensive regional classification to support the formulation of targeted development policies. This study aims to classify Indonesian provinces based on their poverty and development characteristics. The data used are secondary data for the year 2024 obtained from Statistics Indonesia (Badan Pusat Statistik), with 38 provinces as the units of analysis. The variables include the poverty rate, Human Development Index (HDI), Open Unemployment Rate (OUR), and gross regional domestic product (GRDP) per capita. The analytical method employed is K-Means clustering, with variables standardized using Z-Scores. The optimal number of clusters was determined using the Elbow method and confirmed by the Silhouette Score. The results indicate that Indonesian provinces can be grouped into four clusters with distinct social and economic characteristics. Each cluster reflects different levels of poverty, human development quality, and labor market conditions. These findings emphasize that poverty in Indonesia is a multidimensional issue, underscoring the need for development and poverty alleviation policies that are tailored to the specific characteristics of each cluster.