<![CDATA[Stunting in children under five is a chronic nutritional disorder whose risk increases significantly in post-disaster settings due to disruptions in food supply chains, damage to health infrastructure, and deteriorating sanitation conditions. Delays in identifying children at high risk of stunting in evacuation centers prevent targeted, timely, and efficient emergency nutrition interventions. This study proposes a post-disaster stunting risk prediction system based on Prepared Machine Learning (XML), which integrates a predictive model based on ensemble learning with the SHapley Additive exPlanations (SHAP) interpretability framework to support transparent and evidence-based emergency nutrition intervention decision-making. The model is built with Explainable Artificial Intelligence (XAI) to make the stunting prediction system transparent and understandable. SHAP is used as the primary method to explain the contribution of each factor to stunting risk prediction. Data were processed using anthropometric and sociodemographic datasets of children aged 0–59 months, consisting of victim data collected from the three provinces of Aceh, North Sumatra, and West Sumatra, data from the Center for Disaster Management (CDC), and data and information from the Indonesian National Disaster Management Agency (BNPB). Twelve input features were used, including height-for-age (HAZ) z-score, weight-for-age (WAZ) z-score, exclusive breastfeeding, frequency of food intake, immunization status, household sanitation conditions, family income level, and duration of displacement. Model performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUC-ROC), F1-Score, precision, and recall, with stratified tenfold cross-validation to ensure model generalizability. SHAP-based interpretability analysis identified the five features with the highest predictive contribution as HAZ z-score, duration of displacement, frequency of food intake, exclusive breastfeeding, and sanitation conditions, which consistently align with established stunting risk factors in the nutrition science literature. The system then interfaces into a web-based decision support interface that displays SHAP power plots per child and population-level least squares plots, enabling field nutrition workers without a data science background to understand the rationale behind each risk prediction. This study provides an explainable and operationally applicable machine learning framework for the Ministry of Health, BNPB, and National humanitarian agencies as a transparent, auditable, and accountable decision-support instrument for emergency nutrition interventions in post-disaster situations.]]>
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