Strategic Clustering of Poverty Areas in Central Java Using K-Means and Silhouette Evaluation - Tacharri, Chusnuut; Rohmani, Asih; Fahmi, Amiq (Politeknik Ganesha Medan, 2025)
Indonesia is one of several developing nations that struggle with poverty. Central Java is one of Indonesia's provinces with the third-highest percentage of the country's inadequate. This study aims to explore and improve the application of the K-Means Algorithm in investigating socioeconomic disparities. In this study, the Elbow method is used to determine the optimal number of clusters to overcome the weaknesses in determining the number of clusters in conventional K-Means. Model evaluation using the silhouette coefficient shows the effectiveness of this method approach with a value of 0.504 and several clusters (K = 3), which meets the medium structure category. The Human Development Index (HDI) and Uninhabitable Households (RTLH) were two criteria used to categorize poverty areas using the K-Means Algorithm optimization successfully. According to the clustering results, there were 12 regions in Cluster 0, 2 in Cluster 1, and 21 in Cluster 2. These findings are anticipated to offer the Central Java Provincial Government critical insights, facilitating the development of precise and well-targeted initiatives to address deprivation issues effectively. Furthermore, a more systematic and structured optimization of the K-Means algorithm has the potential to significantly improve both the accuracy and practical relevance of studies on socioeconomic inequality in Central Java Province. This enhanced methodological approach can provide more in-depth results on data-driven regional disparities to reduce these disparities comprehensively.