Analisis Klasterisasi Mengenai Tingkat Prevelensi Stunting di Jawa Barat Tahun 2023

Abstract

This study analyzes the prevalence rate of stunting in 27 districts/cities in West Java in 2023 using the K-Means Clustering algorithm. Stunting is a serious health problem that affects the quality of human resources, especially in areas with low sanitation access and socio-economic conditions. With data on stunting prevalence, sanitation access, and poverty levels, this study groups areas into four clusters based on similar characteristics. Clustering aims to map priority intervention areas to support data-based policies. The results show that areas with low sanitation access tend to have a high prevalence of stunting. Clustering produces important insights, such as the relationship between socio-economic conditions and stunting distribution patterns, as well as sanitation access. The Elbow and Silhouette Plot methods were used in R-Studio to assess the quality of clustering, resulting in an average silhouette width of 0.33 with 4 clusters. And the data also uses Python to be analyzed and visualized in two-dimensional space using the Principal Component Analysis (PCA) method. This visualization maps data into five different clusters. This study makes a significant contribution to local governments in formulating area-based intervention policies. With this approach, research is expected to support efforts to reduce the prevalence of stunting in West Java effectively.