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This study compares K-Means and Hierarchical Clustering methods to identify regional welfare disparities in West Java Province using various public welfare indicators. K-Means Clustering with five clusters achieved the highest silhouette score of 0.219, while Hierarchical Clustering with Ward Linkage and eight clusters scored 0.202. The findings highlight distinct welfare challenges across different clusters, providing valuable insights for policymakers in developing targeted strategies.

Author

talontalonn1

Language

EN