<![CDATA[Sugar consumption in Indonesia remains high, with diabetes affecting 20.4 million people. This condition has prompted the government to introduce an excise policy on Minuman Berpemanis Dalam Kemasan (MBDK) to reduce sugar intake. Social media, particularly the X platform, serves as a medium for the public to express their opinions regarding this policy. This study aims to analyze public sentiment toward the MBDK excise policy using a lexicon-based approach for data labeling and the Multinomial Naive Bayes algorithm with unigram and bigram feature extraction. The initial results show that the highest performance was achieved using 5-Fold Cross Validation, with an average accuracy of 83%, precision of 84%, recall of 75%, and an F1-Score of 77%. After applying data balancing using Stratified Cross Validation combined with Borderline-SMOTE and limiting the features to the 700 most frequent terms, the model’s performance improved. The best results were obtained with 10-Fold Cross Validation, achieving 86% accuracy, 84% precision, 83% recall, and an F1-Score of 83%. These findings indicate that the Multinomial Naive Bayes model can effectively classify public sentiment regarding the MBDK excise policy after the data balancing process.]]>
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