K-Means Approach to Identifying High-Risk Stunting Areas in Indonesia - Hairul Aji; Femi Dwi Astuti (bit-Tech, 2025)
Stunting is one of the chronic nutritional problems that remains a serious issue in Indonesia, particularly in Bulungan Regency, North Kalimantan. This condition not only affects children's physical growth but also their cognitive development, which has implications for their quality of life and future prospects. This study aims to classify regions based on stunting risk levels to provide a more targeted framework for local governments in setting intervention priorities. The method used is K-Means Clustering, an effective data mining algorithm for non-hierarchical data clustering. The data used are secondary data from the Bulungan District Bappeda in 2021, covering 81 locations with 29 stunting risk factor variables. The analysis process was conducted through data processing, centroid initialization, Euclidean distance calculation, and the formation of convergent clusters. The results of the study show the formation of two main clusters: a cluster with moderate vulnerability and a cluster with high vulnerability. The moderate cluster is in a transitional state with fluctuating risks, while the high cluster has low health and sanitation indicators, requiring special attention. These findings indicate that the K-Means method can provide data-driven insights to support stunting prevention policies. This study is expected to serve as a reference for local governments in developing more targeted intervention programs and contribute academically to the application of data mining methods in public health.