<![CDATA[Program Makan Bergizi Gratis (MBG) has triggered extensive discourse on social media platform X, which serves as a primary space for public expression of opinions toward government policies. This study aims to analyze public sentiment toward the MBG program while simultaneously comparing the performance of two prominent Transformer-based models, namely IndoBERT-Large and NusaBERT-Large. This research adopts a quantitative approach employing supervised learning on 10,201 Indonesian-language posts (tweets) collected through web scraping from February 2024 to September 2025. A total of 2,000 samples were manually annotated as ground truth, achieving a high level of inter-annotator reliability (Cohen’s Kappa, κ = 0.81). The experimental results indicate that IndoBERT-Large outperforms NusaBERT-Large, achieving an accuracy of 83.00%, while NusaBERT-Large demonstrates competitive performance with an accuracy of 80.50%. Substantively, public discourse is dominated by negative sentiment, accounting for nearly 50% of the total data, reflecting public concerns regarding budgetary constraints and technical implementation issues. Positive sentiment ranges between 33% and 36%, indicating sustained and substantial public support for the program. These findings confirm the effectiveness of Transformer-based models in accurately capturing the dynamics of public opinion toward government policies using social media data.]]>
Copyrights © 2026
