<![CDATA[Food distribution in the Free Lunch Program (MBG) requires timeliness to maintain food quality and service effectiveness to beneficiaries. Delays can be influenced by distribution distance, number of recipients, weather, road conditions, delivery time, and vehicle type. This study aims to develop a machine learning-based prediction model for the risk of delays in MBG food distribution. The research method uses a quantitative approach with a dataset of 100 data samples from operational distribution scenarios in Soppeng Regency. Data were processed through cleaning, categorical variable coding, numeric variable normalization, 5-fold cross-validation splitting, model training, and performance evaluation. Four algorithms were compared: Random Forest, Decision Tree, K-Nearest Neighbor, and Logistic Regression. The test results showed that Random Forest achieved 92.00% accuracy, 92.00% precision, 92.00% recall, and 92.00% F1-score. Feature importance analysis showed that the number of recipients, distribution distance, and distribution time were the most dominant factors in determining the risk of delays. The proposed prediction system can be a tool for MBG distribution managers in identifying potential delays early and formulating more appropriate operational mitigation recommendations.]]>
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