Perbandingan Metode Elbow dan Silhouette Coefficient pada K-Means untuk Pengelompokan Wilayah Berdasarkan Indeks Pembangunan Manusia - Shinta Zahira Hayathun Nufus; Fathir Fathir; Hilyatul Mustafidah (Building of Informatics, Technology and Science (BITS), 2026)
One of the primary metrics for evaluating the effectiveness of efforts to improve people’s well-being through human development is the Human Development Index (HDI). Although Indonesia’s HDI has continued to improve, disparities in human development remain evident across regions, particularly in Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). Identifying regions with similar HDI characteristics is important for supporting more targeted development policies. However, the performance of K-Means clustering is highly influenced by the number of clusters used, making the selection of an appropriate cluster number essential. This study compares the Elbow Method and Silhouette Coefficient in determining the optimal number of clusters for 2024 HDI data covering 41 regencies and municipalities based on Life Expectancy, Expected Years of Schooling, Mean Years of Schooling, and Per Capita Expenditure. The results show that the Elbow Method produces three clusters, while the Silhouette Coefficient produces two clusters with a silhouette value of 0.5312. Evaluation using the Davies–Bouldin Index (DBI) indicates that the two-cluster solution achieves a lower DBI value (0.7350) than the three-cluster solution (1.0382). These findings suggest that the HDI structure in Bali, NTB, and NTT tends to form two major groups: regions with high human development and regions with medium-to-low human development. The results also indicate that the Silhouette Coefficient is more representative for determining the optimal number of clusters in HDI data with relatively similar regional characteristics. The clustering results may support policymakers in prioritizing development programs in education, health, and community welfare