<![CDATA[Public sentiment toward government programs is increasingly expressed through social media, necessitating robust quantitative evaluation methods. This study examines public sentiment toward Indonesia's Free Nutritious Meal (Makan Bergizi Gratis/MBG) program using 7,958 manually annotated Indonesian-language posts from platform X (January-August 2025), consisting of 3,752 positive, 848 negative, and 3,358 neutral tweets. Sentiment classification was conducted using IndoBERT-base-P2 and compared with a Support Vector Machine (SVM) baseline with TF-IDF features, employing class-weighted learning to address data imbalance. Model performance was evaluated using accuracy and macro F1-score, followed by paired-sample statistical testing. IndoBERT-base-P2 achieved 92% accuracy and a macro F1-score of 0.90, outperforming SVM (86% accuracy, macro F1 = 0.83). Paired t-test results indicate that this improvement is statistically significant (p < 0.05), confirming the robustness of transformer-based modeling. This study contributes methodologically by integrating contextual language modeling, imbalance-aware optimization, and inferential statistical validation within a unified sentiment analysis framework, demonstrating the quantitative advantage of transformer-based approaches for Indonesian social media policy analysis.]]>
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