Segmentasi Risiko Kesehatan Bayi dan Balita Menggunakan Algoritma K-Means - Lalu Mutawalli; Mohammad Taufan Asri Zaen; Ahmad Tantoni; Indi Febriana Suhriani (Bulletin of Computer Science Research, 2025)
Urban disparities in maternal and child health remain a critical challenge in achieving the Sustainable Development Goals (SDGs). This study aims to map the disparity of health risks among infants and toddlers across 44 subdistricts in DKI Jakarta by analyzing three key indicators: prevalence of low birth weight (LBW), infant mortality, and undernutrition. Cross-sectional data from 2024 (n=176) were normalized using Min-Max scaling to minimize scale bias. The clustering process using the K-Means algorithm was conducted after determining the optimal number of clusters (k=5) through the Elbow method. Cluster validation employed three metrics—Silhouette Score (0.65), Davies-Bouldin Index (0.45), and Calinski-Harabasz Index (82.2)—demonstrating the model's consistency. Stability analysis through subsampling further confirmed the reliability of the results (standard deviation <0.1). Five risk patterns were identified: (1) two low-risk clusters (LBW <1.0%; undernutrition <2%), (2) two moderate-risk clusters (LBW 1.0–1.75%; infant mortality 0.5–3%), and (3) one high-risk cluster (LBW >1.75%; undernutrition >8%). The subdistricts of Jagakarsa and Kepulauan Seribu were identified as priority intervention hotspots due to high comorbidity risks. The findings indicate that the K-Means approach is effective in supporting evidence-based resource allocation policies, particularly in optimizing NICU services and nutrition supplementation programs in high-risk areas. This spatially based approach also facilitates more intuitive visualization for targeted and efficient planning of local health programs.