<![CDATA[Evaluating the nutritional adequacy of meal menus in nutrition fulfillment programs is generally performed manually, which is time-consuming, requires precision, and is prone to recording errors. This study aims to develop an integrated information system for classifying the nutritional adequacy of meal menus with an Artificial Neural Network (ANN)-based classification module. The system unifies menu data management, automatic nutrient computation, adequacy classification, and analysis history storage within a single platform. The ANN module was trained on a reproducibly generated controlled synthetic dataset of 4,500 samples with three balanced classes (Insufficient, Adequate, and Excessive) and ten nutrient adequacy percentage features. Testing covered model performance using accuracy, precision, recall, F1-score, balanced accuracy, and the confusion matrix; functional black-box testing; and usability testing with the System Usability Scale (SUS). The classification module achieved a test accuracy of 98.81%, a macro F1-score of 98.81%, and a balanced accuracy of 98.81%; all black-box scenarios were valid (100%), and the mean SUS score was 88.50 (grade A+). These results show that the system can automate nutritional adequacy evaluation consistently and in a documented manner. The main contribution lies in integrating menu data management, nutrient computation, and automatic classification within one system, rather than in proposing a new classification method.]]>
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