<![CDATA[Stunting remains a major nutritional problem that requires accurate and reliable assessment of children’s current growth status. Machine learning can support stunting classification; however, class imbalance and limited model interpretability remain important challenges. This study aims to evaluate the effect of SMOTEENN across classification algorithms with different learning characteristics and interpret the best-performing model using SHAP. Two secondary datasets were integrated, resulting in 41,176 records, of which 41,173 were retained after data cleaning and numerical conversion. Logistic Regression, Decision Tree, Random Forest, and XGBoost were evaluated using original and SMOTEENN-balanced training data. Hyperparameter optimization was performed using RandomizedSearchCV with stratified five-fold cross-validation and ROC-AUC as the optimization metric. The optimized configurations were fixed and applied to both experimental scenarios. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC on an independent test set. Random Forest trained on the original data achieved the best overall performance, with an Accuracy of 0.9600, Precision of 0.9584, F1-Score of 0.9505, and ROC-AUC of 0.9927. SMOTEENN consistently improved Recall across all models but generally reduced the overall performance of tree-based models. The largest improvement occurred in Logistic Regression, with Recall increasing from 0.7058 to 0.8657. SHAP analysis identified Height and Age (Month) as the most influential features. These findings show that the effect of SMOTEENN depends on dataset and algorithm characteristics, while SHAP provides transparent interpretation of current stunting-status classification within the analyzed datasets.]]>
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