<![CDATA[The Free Nutritious Meal Program is one of the Indonesian government programs aimed at improving community nutritional quality and reducing stunting rates. The program has generated various publik responses expressed through media social platforms, particularly in YouTube comment sections. This study was conducted to analyze publik sentiment toward the Free Nutritious Meal Program (MBG) and to compare the performance of the Naive Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment from YouTube user comments. The research data were obtained through a YouTube comment scraping process and then processed through several preprocessing stages, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Furthermore, feature weighting was performed using the TF-IDF method, and data labeling was carried out using a lexicon-based approach. The sentiment classification process employed the Naive Bayes and Support Vector Machine (SVM) algorithms, while model evaluation was conducted using confusion matrix, accuracy, precision, precision, and f1-score metrics. The results showed that the Support Vector Machine (SVM) algorithm achieved better performance than Naive Bayes. The SVM algorithm obtained an accuracy of 77.4%, precision of 78.4%, precision of 77.4%, and f1-score of 77.6%, whereas the Naive Bayes algorithm achieved an accuracy of 70.5%, precision of 74.4%, precision of 70.5%, and f1-score of 67.7%. The main contribution of this study is the comparative evaluation of Naive Bayes and Support Vector Machine (SVM) for classifying public sentiment from YouTube comments related to the MBG program, providing empirical evidence on the most effective classification approach for supporting social media–based public opinion analysis of government policies.]]>
