Sentiment Analysis of Indonesian Tweets on the Free Lunch and Milk Program Using TF-IDF and Naïve Bayes
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Abstract
This study analyzes public sentiment expressed in Indonesian-language tweets concerning the Free Lunch and Milk Program using Term Frequency–Inverse Document Frequency (TF-IDF) and the Multinomial Naïve Bayes algorithm. A total of 964 relevant tweets were collected from X and classified into positive and negative sentiment categories. The research process consisted of data collection, text cleaning, normalization, tokenization, stopword removal, stemming, TF-IDF feature extraction, model training, and performance evaluation. The dataset comprised 500 positive tweets and 464 negative tweets, indicating a relatively balanced class distribution. The proposed model achieved an accuracy of 88%, precision of 85%, recall of 82%, and an F1-score of 83%. Confusion matrix analysis indicated that most tweets were correctly classified, although errors remained in tweets containing sarcasm, irony, mixed sentiment, and indirect policy references. These findings demonstrate that the combination of TF-IDF and Naïve Bayes provides an efficient and interpretable baseline for monitoring public opinion on Indonesian social policy. Nevertheless, more contextual language models are required to improve the classification of complex and implicit sentiment expressions.
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