<![CDATA[Stunting remains a significant health issue in Indonesia due to its long-term impact on human resource quality and economic productivity. Despite various intervention programs, disparities in stunting rates between regions remain high, particularly in areas with diverse socioeconomic conditions. This study aims to identify patterns and group regions based on stunting risk levels using two machine learning approaches: Hierarchical Clustering (HC) and Deep Embedded Clustering (DEC). The data used are aggregated data from toddler measurements, including the number of toddlers measured, the number of stunting cases, and the percentage of stunting in the 2020–2024 period. The analysis was conducted by comparing the cluster results from the two methods. The HC method is implemented using an Agglomerative Clustering approach with the Ward linkage criterion, while DEC uses a layered autoencoder architecture optimized through Kullback–Leibler divergence. To assess cluster quality, the study uses the Silhouette Score metric. The results showed that HC produced the highest Silhouette score of 0.5430, while DEC reached 0.4874, with a year-on-year performance trend. These findings indicate that HC excels in clustering stability, while DEC is more adaptive to data complexity and nonlinear patterns. The combination of the two has the potential to support the formulation of more comprehensive, data-driven policies to identify and address stunting-prone areas.]]>
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