Stunting Risk Cluster Analysis In Petatal Plantation Village Using K-Means Clustering Approach
DOI:
https://doi.org/10.24235/itej.v11i1.299Keywords:
stunting risk, cluster analysis, K-Means Clustering, data mining, Petatal Plantation Village, public healthAbstract
Stunting remains a critical public health issue in rural communities, particularly in plantation-based villages where socioeconomic conditions, nutrition access, sanitation, maternal knowledge, and health service utilization may vary across households. This study aims to analyze stunting risk clusters in Petatal Plantation Village using the K-Means Clustering approach. The research applies a quantitative data mining method by grouping household or child-level data based on several risk indicators, including child age, nutritional status, birth weight, exclusive breastfeeding history, maternal education, household income, access to clean water, sanitation conditions, immunization status, and frequency of visits to health service facilities. The K-Means algorithm was used to classify the data into several clusters representing different levels of stunting risk. The clustering process involved data preprocessing, normalization, determination of the optimal number of clusters, model implementation, and interpretation of cluster characteristics. The results of the study are expected to identify distinct risk groups, such as low-risk, moderate-risk, and high-risk clusters. Households in the high-risk cluster are generally characterized by limited economic capacity, poor sanitation, low maternal nutrition awareness, inadequate dietary diversity, and irregular access to health services. Meanwhile, the moderate-risk cluster may show partial vulnerability, while the low-risk cluster reflects better nutritional and environmental conditions. This clustering analysis provides a data-driven basis for village authorities, health workers, and local stakeholders to design more targeted stunting prevention programs. Instead of applying a uniform intervention, the proposed approach supports priority-based decision-making according to the specific characteristics of each risk cluster. Therefore, K-Means Clustering can be considered an effective analytical tool for mapping stunting vulnerability and strengthening evidence-based public health intervention strategies in Petatal Plantation Village.
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