Analyzing Public Sentiment Toward the Makan Bergizi Gratis (MBG) Program on TikTok Using SVM and IndoBERT - Alfredo Winston; Nicholas Darren; Henry Lucky; Rilo Pradana; Noviyanti Sagala (Bina Nusantara University, 2026)
Social media has become a major platform for the public to express opinions toward government programs. This study analyzes public sentiment toward Indonesia’s Makan Bergizi Gratis (MBG) program using a text mining approach. A total of 11,730 TikTok comments related to the MBG program were collected and classified into positive, negative, and neutral sentiments. Two classification models were compared: a traditional Support Vector Machine (SVM) using TF-IDF features and a transformer-based model, IndoBERT. Experimental results show that IndoBERT outperforms the tuned SVM model, achieving an accuracy of 0.78 and a weighted F1-score of 0.78, compared to 0.73 accuracy and 0.73 F1-score obtained by the SVM. IndoBERT demonstrates better performance in handling neutral and context-dependent sentiments, indicating its effectiveness for analyzing Indonesian social media data related to public policy evaluation. This study contributes to the growing body of research on Indonesian sentiment analysis by providing an empirical comparison between classical machine learning and transformer-based models for analyzing public responses to government policies using social media data. The findings also highlight the importance of advanced language models in capturing linguistic nuances, informal expressions, and contextual meanings commonly found in online discussions, particularly on rapidly evolving social media platforms widely used by Indonesian users across different demographic and social backgrounds.