<![CDATA[The free nutritious meal program is a government initiative to improve public health and reduce stunting in Indonesia. However, planning daily combinations that balance nutrition, variety, and recipient preferences remains a challenge. This study applies data mining techniques using the Apriori Algorithm to discover optimal food menu combinations based on functional nutritional standards. The data analyzed includes historical food distribution records, nutritional content, and consumer satisfaction. Through data preprocessing and setting minimum support and confidence thresholds, strong association rules between food items were identified. The results demonstrate that the Apriori Algorithm successfully extracts significant patterns; for instance, a menu containing "White Rice" and "Fried Chicken" shows an 85% confidence level of being paired with "Vegetable Soup" and "Banana." In conclusion, the Apriori Algorithm effectively provides adaptive and varied menu recommendations without compromising balanced nutrition. This system serves as a practical decision-making tool for schools and community kitchens to optimize weekly meal scheduling, manage budgets efficiently, and minimize food waste.]]>
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