<![CDATA[The Health Department is an integral part of the government structure with broad responsibilities for managing various aspects of health, including daily medical services activities that contribute to maintaining a healthy nutritional balance, reflected in good quality processed foods. Therefore, there is a need for grouping and classifying processed foods. The problem addressed in this research is the difficulty faced by the Palembang Health Department in determining the classification of processed food products. Hence, the researcher employed the Naive Bayes method and RapidMiner Software as supporting software tools in this study. The study found that in Accuracy testing, both RapidMiner and manual Naive Bayes calculations yielded the same result of 94.52% for the positive class: Plant-based. In Precision testing, there was a difference between RapidMiner and manual Naive Bayes calculations, with RapidMiner yielding a higher result of 93.22% for the positive class: Plant-based, while manual Naive Bayes calculation obtained a value of 78%. Similar values were also obtained in Recall testing, where both RapidMiner and manual Naive Bayes calculations yielded the same result of 100% for the positive class: Plant-based. In F1 Score testing, both RapidMiner and manual Naive Bayes calculations yielded the same result of 100%. In RapidMiner's Area Under The Curve (AUC) testing, the result obtained was 0.973 (positive class: Plant-based), and the high accuracy of the ROC/AUC curve indicates "Excellent Classification", suggesting that the use of plant-based food materials dominates over the use of animal-based food materials. It is hoped that the results of this research can assist the Health Department in classifying food processing categories. Keywords : Naive Bayes, Classification, Processed Food, Palembang City Health Department, Food Product Supervision, Food Materials, Animal-based, Plant-based.]]>
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