A Comparative Performance Evaluation of Unsupervised Learning Algorithms for Clustering Stunting Prevalence in Aceh Province - Novia Hasdyna; Rozzi Kesuma Dinata; Baringin Sianipar (Sisfo: Jurnal Ilmiah Sistem Informasi, 2026)
Stunting remains a significant public health issue in Indonesia, particularly in Aceh Province, where considerable disparities continue to exist across districts and municipalities. Identifying regional prevalence patterns is crucial for developing evidence-based intervention strategies. This study assesses the performance of four unsupervised learning algorithms, namely K-Means, Hierarchical Clustering, Gaussian Mixture Model (GMM), and Fuzzy C-Means (FCM), for clustering district-level stunting data in Aceh Province across five observation periods. Algorithm performance was evaluated using the Calinski-Harabasz Index, convergence efficiency, and cluster interpretability. The findings demonstrate that Fuzzy C-Means outperformed the other methods, achieving the highest Calinski-Harabasz score of 49.75, followed by GMM with 42.61, Hierarchical Clustering with 36.48, and K-Means with 25.30. In addition, FCM showed the fastest convergence, requiring only three iterations. Three stable regional clusters were identified, representing high, moderate, and low prevalence levels. High-prevalence areas included Aceh Barat, Aceh Utara, Aceh Tenggara, Pidie Jaya, Aceh Barat Daya, Simeulue, and Bener Meriah, whereas Subulussalam constituted the low-prevalence cluster. These findings indicate that Fuzzy C-Means provides a reliable approach for regional stunting classification and may contribute to more targeted policy interventions in Aceh Province.