<![CDATA[Stunting is a chronic nutritional disorder that impairs children's cognitive development and longterm productivity, making early risk identification essential. However, previous studies have mainly focused on stunting classification or conventional machine learning models, with limited attention to integrating probability threshold optimization and explainable artificial intelligence (XAI) for low birth length risk prediction. This study proposes a predictive framework using Random Forest (RF) and XGBoost based on 249,626 valid records from the 2024 Indonesian Nutritional Status Survey (SSGI), where low birth length was defined as a birth length below 48 cm. The models were optimized through hyperparameter tuning, probability threshold optimization, Stratified 5-Fold Cross Validation, and SHAP analysis. Evaluation on 10,000 testing records showed that Random Forest accuracy increased from 76.76% to 78.58%, while XGBoost achieved the highest accuracy of 84.07% at a threshold of 0.79, although recall decreased to 27%. SHAP identified birth weight as the most influential predictor. These findings demonstrate that threshold optimization improves predictive accuracy, while SHAP enhances model interpretability to support early screening and clinical decision-making for low birth length risk]]>
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