K-Means Clustering Optimization of Toddler Malnutrition Status Using Elbow Method
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Abstract
The problem of nutritional status is still a major challenge in the health sector in developing countries, including Indonesia. Malnutrition in toddlers can have serious long-term impacts on children's growth and development, including increased risk of disease, impaired cognitive function, and low productivity in the future. To overcome this problem, an in-depth analysis is needed to determine the distribution of nutritional status of toddlers in one of the provincial capitals in Indonesia, which can be used as a basis for planning more effective interventions. This study uses the K-Means algorithm to classify areas based on the prevalence of malnutrition in toddlers across all sub districts in the city. Determination of the optimal number of clusters was carried out using the Elbow method, which showed that the most appropriate clusters were two clusters. To assess the quality of the cluster, the Davies Bouldin Index (DBI) was used which produced a score of 0.361, while the Silhouette Score was 0.799, indicating that the cluster results were of high quality. The clustering results showed significant variations in the prevalence of malnutrition in various sub districts. Cluster 0 represents areas with low prevalence of malnutrition, comprising six sub districts, while Cluster 1 includes ten sub districts with high prevalence of malnutrition. By identifying these high-risk areas more clearly, health authorities and practitioners can develop more targeted and effective nutrition interventions. This research highlights the importance of data driven decision making in public health, supporting augmented intelligence in identifying and addressing nutritional problems in urban areas. The insights provided by this clustering approach contribute to more efficient and strategic health intervention planning.
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