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All Journal Journal of Computing and Smart Ecosystems
Inas Nilasari
Airlangga University
Computer Science & IT Electrical & Electronics Engineering

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Provincial Classification of Child Malnutrition in Indonesia: A K-Means Clustering Approach for Evidence-Based Stunting Intervention Prioritization Inas Nilasari; Nia Eka Safitri
Journal of Computing and Smart Ecosystems Vol. 2 No. 1 (2026): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jcase.v2i1.1144

Abstract

Indonesia's stunting prevalence of 21.5% recorded in SSGI 2024 surpasses the WHO emergency threshold, yet provincial data are conventionally presented in tabular formats that obscure spatial risk patterns and constrain evidence-based resource allocation. This study applies hierarchical clustering to 35 Indonesian provinces using five child nutritional indicators that is stunting, underweight, severe wasting, wasting, and overweight drawn from SSGI 2024. Four linkage methods were evaluated across k = 2 to 7 using Silhouette Score, Dunn Index, Connectivity, and Cophenetic Correlation Coefficient. Ward.D2 at k = 2 yielded the optimal configuration, producing two substantively distinct clusters: Cluster 1 (18 provinces) with comparatively lower undernutrition burden (mean stunting 17.98%, mean wasting 5.72%), and Cluster 2 (17 provinces) with markedly elevated risk across all undernutrition indicators (mean stunting 26.60%, mean wasting 9.11%). Papua consistently emerged as a structurally outlying unit across all linkage methods. These findings provide an empirical basis for geographically differentiated nutritional policy, enabling priority deployment of emergency interventions in high-burden provinces while sustaining preventive programming elsewhere.
Co-Authors Nia Eka Safitri