Comparison of K-Means and Ward Methods in Clustering Indonesian Provinces Based on Household Basic Service Access - Nurul Mulya; Fajri Juli Rahman Nur Zendrato; Muhammad Arief Rivano; Zamahsary Martha; Tessy Octavia Mukhti (UNP Journal of Statistics and Data Science, 2026)
Disparities in household basic service access across provinces in Indonesia remain a key issue in regional development. Basic services such as access to improved drinking water, proper sanitation, electricity, and adequate housing are essential indicators of household welfare, making regional classification necessary to identify similarities and disparities among provinces. This study aims to cluster Indonesian provinces based on household basic service access indicators and to compare the performance of the K-Means method and Hierarchical Clustering using the Ward approach. The analysis was conducted using numerical data with Euclidean distance as a measure of similarity. The optimal number of clusters was determined using the Silhouette plot and further validated using the Silhouette Coefficient. The results indicate that both K-Means and Ward methods produce two optimal clusters representing provinces with relatively high and relatively low levels of household basic service access. Centroid analysis reveals clear differences between clusters across all indicators, particularly in electricity access and sanitation. Furthermore, the evaluation of clustering quality shows that the Ward method yields a higher Silhouette Coefficient than the K-Means method, indicating more compact clusters and better separation between clusters. Therefore, the Ward method is considered more effective in mapping patterns of household basic service access across provinces. The findings of this study can support regional planning by providing a clearer understanding of disparities in household basic service access in Indonesia.