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Nicea Roona Paranoan
Cenderawasih University, Indonesia
Decision Sciences, Operations Research & Management Mathematics

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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 Vol. 26 No. 1 (2026): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v26i1.8819

Abstract

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.
Analysis of Stunting Data in Indonesia Using K-Means and Self Organizing Map (SOM) Caecilia Bintang Girik Allo; Nicea Roona Paranoan; Winda Ade Fitriya B; Bobi Frans Kuddi; Feby Seru
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.7778

Abstract

Abstract. Stunting is a global public health concern, including in Indonesia. The Indonesian government establishes a target for stunting prevalence reduction every year. The government is aiming for a stunting prevalence of 18% in 2025. The government certainly requires policy recommendations to achieve this target. Clustering analysis can be used to identify provinces with similar characteristics or those that still require special attention based on stunting related indicators. There are several clustering methods, including K-Means and Self-Organizing Map (SOM). This study aims to classify provinces in Indonesia based on indicators related to stunting and to compare the performance of two clustering methods. Based on the obtained data, it was found that the data contains outliers. The best clustering method can be determined using the Silhouette Coefficient (SC) and Davies Bouldin Index (DBI). The results showed that the highest SC value, 0.62, was obtained using the SOM method and the lowest DBI, 0.75, was obtained also using SOM method. Two clusters were formed using the SOM method. Cluster 1 consisted of 36 provinces in Indonesia. Cluster 2 consisted of 2 provinces, namely Highland Papua and Central Papua.
Co-Authors Bobi Frans Kuddi Caecilia Bintang Girik Allo Dina Jumiatul Fitri Feby Seru Tiku Tandiangnga Winda Ade Fitriya B Winda Ade Fitriya B.