Sentimen Publik di Media Sosial Terhadap Program Makan Bergizi Gratis (Computational Content Analysis pada Komentar Netizen di Akun Instagram tempodotco)

This research is motivated by the existence of MBG as a government priority program, which aims to fulfill the nutritional needs of the community, especially school children. However, in its implementation, various polemics have emerged, such as cases of food poisoning and the disbursement of funds that is considered less transparent. From these polemics, various public responses to the Makanan Bergizi Gratis Program (MBG) have emerged as reported through social media, particularly Instagram. The comment section on media accounts has become a space for public expression that shows support, criticism, and responses to policy issues. This study aims to analyze the distribution of public sentiment and identify the main themes that appear in netizen comments on posts from the Instagram account @tempodotco regarding the MBG Program.

This study uses a quantitative approach with the Computational Content Analysis (CCA) method, based on the concept of social media as a digital public sphere. The research data consist of 492 netizen comments collected from @tempodotco posts related to the MBG Program, which were then analyzed through the stages of data collection, text cleaning, normalization, sentiment classification, and interpretation of the results. The results of the study show that negative sentiment dominates public conversation, with 293 comments (59.6%), followed by neutral sentiment with 130 comments (26.4%), and positive sentiment with 69 comments (14.0%). These findings indicate that public responses to news coverage of the MBG Program tend to be critical. In addition, the main conversation themes that emerged include program implementation, transparency and management, the involvement of political actors, and the social objectives of the program in fulfilling the nutritional needs of school children. This study shows that Instagram comments on media accounts can serve as an important data source for understanding the dynamics of public opinion toward government policies in the digital space.