<![CDATA[Economic growth in Indonesia varies significantly between provinces, reflecting disparities in welfare indicators such as poverty levels, education, and access to infrastructure. Understanding these disparities is crucial for formulating effective development policies. This study aims to cluster provinces in Indonesia based on economic development indicators 2023, with the dataset sourced from Badan Pusat Statistik (BPS). The research employs K-Means and K-Medoids clustering methods, with the optimal number of clusters determined using the Silhouette method. K-Means produced six clusters, while K-Medoids identified eight clusters. Performance evaluations using the Dunn Index (DI), Davies-Bouldin Index (DBI), and Xie-Beni Index (XBI) revealed that K-Means outperformed K-Medoids, achieving a higher DI (0.31) and lower XBI (1.78). These results indicate that K-Means with six clusters provides better separation and higher intra-cluster density compared to K-Medoids. Profiling of the clusters revealed substantial regional disparities, with some clusters exhibiting high welfare levels and others facing significant challenges in poverty, unemployment, and health issues. Cluster 1 has moderate income and development but high unemployment and health issues. Cluster 2 shows strong development and low poverty but unresolved crime. Cluster 3 has low income, minimal poverty, and health complaints. Cluster 4 excels in income and labor but struggles with poverty and crime. Cluster 5 is prosperous but faces health issues. Cluster 6 has low income, moderate poverty, and significant health challenges. This study aims to assist policymakers in designing tailored strategies to address specific weaknesses and capitalize on regional strengths to reduce disparities and enhance equitable development.]]>
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