Stunting Incidence Segmentation: A Cluster Analysis Approach and Targeted Intervention Strategies

This study develops a data-driven strategy for stunting prevention using the K-Means clustering method, validated through the Elbow Method and Cluster Profiling. The high prevalence of stunting in the research area highlights the need for precise health condition mapping to prioritize effective interventions. Data collected from toddlers in the region were grouped into three distinct clusters, each representing varying levels of risk and requiring tailored prevention strategies. These interventions include contextualized preventive education, optimized based on the specific characteristics and needs of each cluster. The results demonstrate that this method accurately maps health conditions, facilitates targeted interventions, and enhances resource allocation. Additionally, the clustering approach serves as a foundation for creating impactful and relevant health counseling materials to strengthen community education. The study’s main contribution lies in providing a data-driven framework that supports evidence-based public health policy and localized stunting prevention strategies, ensuring adaptability to the unique needs of the research area.