<![CDATA[The "Free Lunch Program," introduced as part of the 2024 Indonesian election campaign, became a hot topic on social media, especially on the platform X. This program aims to improve children's health and nutrition while reducing stunting rates by providing free lunches and milk to children and pregnant women. A study was conducted to analyze public sentiment regarding the program using the Random Forest algorithm. The data consisted of 9,347 tweets collected through web crawling. The analysis revealed that the majority of sentiments were negative (8,021 entries), while positive sentiments accounted for only 430 entries. The preprocessing steps included data cleaning, case folding, tokenization, stopword removal, and stemming. The imbalance between positive and negative sentiment data was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), resulting in a more balanced dataset. After applying SMOTE, the model achieved 100% accuracy, with significant improvements in precision, recall, and F1-Score. The analysis showed that positive sentiments focused on the program's health and educational benefits, while negative sentiments highlighted criticism of implementation and budget allocation. This study demonstrates the value of sentiment analysis in evaluating social programs and understanding public perceptions.]]>
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