<![CDATA[Malnutrition remains a significant public health challenge in Indonesia, with early detection being crucial for effective intervention. Previous studies utilizing the K-Nearest Neighbor (KNN) algorithm demonstrated promising results in classifying malnourished toddlers based on anthropometric data. However, single-model approaches often suffer from sensitivity to noise and limited generalization. This study proposes a hybrid ensemble model combining KNN and Multi-Layer Perceptron (MLP), integrated with mutual information-based feature selection, to improve classification performance. Using a dataset from Puskesmas Ubung, Bali, comprising 1,319 records with nine anthropometric features and a binary malnutrition label, the model was evaluated under stratified five-fold cross-validation. The proposed KNN–MLP ensemble with top-ranked features achieved 94.3% accuracy, surpassing both standalone KNN and MLP models. Additional metrics, including precision (91.7%), recall (89.4%), F1-score (90.5%), and MAE (0.05), confirmed the model's robustness and reliability. These findings demonstrate that ensemble learning combined with feature selection significantly improves early-stage malnutrition classification, offering a scalable approach for decision-support systems in public health interventions.]]>
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