Analyzing Public Sentiment Toward the Makan Bergizi Gratis (MBG) Program on TikTok Using SVM and IndoBERT
DOI:
https://doi.org/10.21512/ijcshai.v3i1.15184Keywords:
Sentiment Analysis, Text Mining, IndoBERT, Support Vector Machine, Public Policy, MBGAbstract
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
References
[1] Kementerian Kesehatan RI, "Pemerintah Perkuat Tata Kelola Program MBG, Keselamatan Anak Jadi Prioritas," [Online]. Available: https://kemkes.go.id/id/pemerintah-perkuat-tata-kelola-program-mbg-keselamatan-anak-jadi-prioritas. [Accessed: Okt. 22, 2025].
[2] Kementerian Pendayagunaan Aparatur Negara Dan Reformasi Birokrasi, "Kementerian PANRB Perkuat Kelembagaan BGN untuk Program MBG yang Semakin Kolaboratif," [Online]. Available: https://menpan.go.id/site/berita-terkini/kementerian-panrb-perkuat-kelembagaan-bgn-untuk-program-mbg-yang-semakin-kolaboratif. [Accessed: Okt. 22, 2025].
[3] Transparency International Indonesia, "MBG Watch Resmi Diluncurkan: Saatnya Publik Pantau Program Makan Bergizi Gratis," [Online]. Available: https://ti.or.id/mbg-watch-resmi-diluncurkan-saatnya-publik-pantau-program-makan-bergizi-gratis/. [Accessed: Okt. 22, 2025].
[4] B. He et al., "Analysis of online public opinion texts based on topic mining and sentiment analysis," Mathematical Modeling and Algorithm Application, vol. 2, no. 2, pp. 23–28, 2024.
[5] R. Suhas and T. Prarthana, "Sentiment analysis using text mining: A survey," International Research Journal of Modernization in Engineering Technology and Science (IRJMETS), vol. 5, no. 8, Aug. 2023, doi: 10.56726/IRJMETS43989.
[6] E. M. Sipayung and B. Hakim, "Sentimen Kebijakan Pemerintah terhadap Kepercayaan Masyarakat Menggunakan Metode NLP Rule Based," JUSTIN (Jurnal Sistem dan Teknologi Informasi), vol. 12, no. 1, pp. 175–181, Jan. 2024, doi: 10.26418/justin.v12i1.72426.
[7] J. C. Setiawan, K. M. Lhaksmana, and Bunyamin, “Sentiment analysis of Indonesian TikTok review using LSTM and IndoBERTweet algorithm,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 8, no. 3, pp. 774–780, Sep. 2023, doi: 10.29100/jipi.v8i3.3911.
[8] K. S. Nugroho et al., "BERT fine-tuning for sentiment analysis on Indonesian mobile apps reviews," in Proceedings of the 10th International Conference on Digital Enterprise and Information Systems (DEIS), 2021, pp. 258–264, doi: 10.1145/3479645.3479679.
[9] F. Fatkhurrohman, B. I. Nugroho, and N. Fadillah, "Analisis Sentimen Program Makan Bergizi Gratis Pemerintah RI Melalui Twitter Menggunakan Metode SVM," Journal of Artificial Intelligence and Digital Business (RIGGS), vol. 4, no. 3, pp. 3906-3917, May 2025, doi: 10.31004/riggs.v4i3.2533.
[10] S. Kurniawan et al., "Text Mining Pre-Processing Using Gata Framework and RapidMiner for Indonesian Sentiment Analysis," IOP Conf. Series: Materials Science and Engineering, vol. 835, no. 1, p. 012057, 2020, doi: 10.1088/1757-899X/835/1/012057.
[11] Y. Asri et al., "Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application," Indonesian Journal of Electrical Engineering and Computer Science, vol. 38, no. 1, pp. 677–688, Apr. 2025, doi: 10.11591/ijeecs.v38.i1.pp677-688.
[12] M.R. Manoppo, I.C. Kolang, D. N. Fiat, R. M. C. Mawara, A. D. P. Sumarno, A. Yusup, and V. Tarigan, "Analisis Sentimen Publik di Media Sosial Terhadap Kenaikan PPN 12% di Indonesia Menggunakan IndoBERT," Jurnal Kecerdasan Buatan dan Teknologi Informasi, vol. 4, no. 2, pp. 152-163, Mei 2025, doi: 10.69916/jkbti.v4i2.322.
[13] R. W. Harwenda et al., "Sentiment analysis on government public policies: A systematic literature review," DIJEMSS: Dinasti International Journal of Education Management and Social Science, vol. 6, no. 5, pp. 4192–4204, Jun. 2025, doi: 10.38035/dijemss.v6i5.
[14] I. Muhandhis and A. S. Ritonga, "Public sentiment analysis on TikTok about Tapera policy using random forest classifier," Sistemasi: Jurnal Sistem Informasi, vol. 14, no. 1, pp. 354–365, Jan. 2025.
[15] A. A. Ningtyas et al., "Analisis sentimen komentar YouTube tentang prediksi resesi ekonomi tahun 2023 menggunakan algoritma Naïve Bayes," Bit (Fakultas Teknologi Informasi Universitas Budi Luhur), vol. 20, no. 1, pp. 9–16, Apr. 2023.
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