Clustering of Stunting Determinants in Papua Province Using Average Linkage Method and K-Means Algorithm - Dina Jumiatul Fitri; Nicea Roona Paranoan; Caecilia Bintang Girik Allo; Tiku Tandiangnga; Winda Ade Fitriya B. (Statistika, 2026)
Abstract. In the current public health context, stunting remains a chronic nutritional problem that requires serious attention in Indonesia, especially in the eastern region such as Papua. This study aims to cluster districts and cities in Papua Province based on factors influencing stunting prevalence using both hierarchical and non-hierarchical clustering approaches, namely the Average Linkage method and the K-Means algorithm. The study acquires secondary data from the 2023 Indonesia Health Survey and the 2024 Statistics Indonesia (BPS) reports, covering nine districts and cities in Papua Province. The variables used include poverty rate, access to proper sanitation, access to safe drinking water, number of midwives, and number of community health centers (puskesmas). The hierarchical clustering with Average Linkage produced two clusters with a Silhouette Coefficient value of 0.239. In comparison, the K-Means method, with the optimal number of clusters determined by the Elbow method and Silhouette analysis, formed three clusters and achieved a higher Silhouette Coefficient value of 0.32, indicating better cluster compactness and separation. The K-Means results provide a more detailed segmentation, distinguishing areas with basic infrastructure inequality, regions with social vulnerability and limited services, and districts with relatively advanced service access and lower poverty. Overall, the K-Means approach offers stronger clustering performance and more granular regional classification. Therefore, clustering results can serve as an evidence-based foundation for designing more targeted regional development and health intervention policies aimed at reducing stunting prevalence in high-risk areas of Papua.