Nanda Arista Rizki, Maulidah Maulidah, Asyril Asyril, Isran K. Hasan, Carolina Fadia Dewi, Dhira Syahlafandi
This study employed K-Means to analyze characteristics of stunting among babies in Samarinda. Clustering areas based on stunted-babies' characteristics allowed the government to prioritize interventions systematically, focusing on regions that required urgent attention and targeting those with higher stunting rates more effectively. The study involved 59 urban villages in Samarinda, covering 26 Puskesmas and 723 Posyandu. The sample consisted of 4880 stunted-babies under 1000 days of life, recorded from August 2022 to July 2023. The study aimed to apply the K-Means method to group neighborhoods based on baby stunting data in Samarinda, utilizing clustering features such as weight, height, and number of babies. The optimal number of clusters was determined using the Elbow method on the WCSS curve. Baby height and weight were transformed using standard normalization, while count data were Box-Cox transformed. The optimal number of clusters was k = 4 with a Silhouette score of 0.5566, indicating a reasonable clustering structure. High-priority clusters near the city center exhibited higher stunting rates and limited healthcare access. In contrast, low-priority clusters near the Mahakam River and highlands showed lower rates, potentially influenced by better natural resources. Additionally, the involvement of Lembaga Pemberdayaan Masyarakat (LPM) in low-priority clusters supported initiatives to improve nutrition and health services effectively. This approach provided a clearer understanding of stunting prevalence and equipped local governments with data-driven insights for designing targeted intervention programs. It urged the government to prioritize groups 1 and 2, which required immediate attention, to ensure more effective targeting. © 2025 Author(s).
Department of Mathematics and Natural Science Education, Universitas Mulawarman, Samarinda, Indonesia; Samarinda City Health Department, Samarinda, Indonesia; Department of Public Policy, Universitas Mulawarman, Samarinda, Indonesia; Department of Mathematics, Universitas Negeri Gorontalo, Gorontalo, Indonesia
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