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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Muhammad Dirga Juang Ikhsan
Universitas Muslim Indonesia
Computer Science & IT Engineering

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STRATIFIKASI PROFIL ANTROPOMETRI BALITA STUNTING MENGGUNAKAN K-MEANS DAN SILHOUETTE SCORE DI PUSKESMAS PAMBOANG Muhammad Dirga Juang Ikhsan; Ihwana As'ad; Herdianti Darwis
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8209

Abstract

Stunting remains a critical public health issue that requires not only prevalence monitoring but also an understanding of variations among affected children. This study aimed to stratify anthropometric profiles among stunted children in the Pamboang Public Health Center area using K-Means with Silhouette Score validation. A quantitative computational approach was applied to secondary anthropometric data recorded in 2025. The initial dataset consisted of 826 records; after removing one technical outlier, 825 records were analyzed. The clustering features included age in months, body weight, and Height-for-Age Z-Score (HAZ), while sex, height, height-for-age category, village/subdistrict, and integrated health post were used for interpretation. The optimal number of clusters was determined by testing K = 2 to K = 10. The highest Silhouette Score was obtained at K = 3, with a value of 0.4011. The clustering results identified three profiles: children with the most severe linear growth impairment, older stunted children with larger absolute body size, and younger stunted children with relatively milder severity. Spatial distribution was examined across the ten villages/subdistricts with the highest number of stunted children. These findings indicate that K-Means can transform routine anthropometric data into structured information to support more targeted growth monitoring based on child profiles and service-area distribution.  
Co-Authors Herdianti Darwis Ihwana As'ad