Sentiment Analysis of the Free Nutritious Meal Program on Twitter Using Naive Bayes and IndoBERT-Based Labeling
DOI:
https://doi.org/10.63158/journalisi.v8i1.1345Keywords:
Free Nutritious Meal Program (MBG), sentiment analysis, IndoBERT, Naïve Bayes, Linear SVCAbstract
The Free Nutritious Meal Program is a government initiative aimed at improving the nutritional status of primary school children in Indonesia. However, its implementation has generated diverse public reactions on the X/Twitter platform, making systematic sentiment analysis essential for policy evaluation. This study analyzes public sentiment using two labeling approaches—translation-based TextBlob and IndoBERT contextual labeling—combined with Naïve Bayes and Linear SVC classifiers. A total of 2,903 Indonesian-language tweets were collected, preprocessed, and classified to compare the performance impact of each labeling method. The evaluation was conducted using accuracy, precision, recall, and macro F1-score. Sentiment distribution under IndoBERT indicates a predominance of negative and neutral opinions, particularly related to budget concerns, implementation quality, and food distribution issues. This study is subject to several limitations. The dataset size (2,903 tweets) and restricted temporal window may limit the generalizability of findings to long-term public discourse. The analysis also relies on a single social media platform (X/Twitter), excluding perspectives from other platforms such as Instagram or TikTok. Moreover, although IndoBERT improves contextual understanding, transformer-based labeling still may not fully capture sarcasm or highly colloquial expressions. Despite these limitations, the study demonstrates the effectiveness of combining Indonesian transformer models with conventional classifiers to support data-driven policy evaluation.
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[1] F. Al isfahani and R. Mubarok, “Analisis sentimen pengguna Twitter terhadap kebijakan pemberlakuan pembatasan sosial berskala besar (PSBB) dengan metode Naïve Bayes,” J. Siliwangi Seri Sains dan Teknol., vol. 7, no. 1, pp. 19–24, 2021.
[2] I. J. T. Gurning, P. P. Adikara, dan R. S. Perdana, “Analisis Sentimen Dokumen Twitter menggunakan Metode Naïve Bayes dengan Seleksi Fitur GU Metric,” J. Pengemb. Teknol. Inf. Ilmu Komput., vol. 7, no. 5, pp. 2169–2177, 2023.
[3] F. M. Sarimole dan Kudrat, “Analisis Sentimen terhadap Aplikasi Satu Sehat pada Twitter Menggunakan Algoritma Naïve Bayes dan Support Vector Machine,” J. Sains Teknol., vol. 5, no. 3, pp. 783–790, 2024.
[4] R. Hidayat, M. Fikry, Yusra, F. Yanto, dan E. P. Cynthia, “Penerapan Naïve Bayes Classifier dalam Klasifikasi Sentimen Publik di Twitter terhadap Puan Maharani,” JUKI J. Komput. Inform., vol. 6, no. 1, pp. 100–108, 2024, doi: 10.53842/juki.v6i1.479.
[5] N. Desiani dan A. Syafiq, “Efektivitas Program Makan Gratis pada Status Gizi Siswa Sekolah Dasar: Tinjauan Sistematis,” Malahayati Nurs. J., vol. 7, no. 1, pp. 27–48, 2025, doi: 10.33024/mnj.v7i1.17497.
[6] U. Agustini dan S. Mulyani, “Efektivitas dan Tantangan Kebijakan Program Makan Bergizi Gratis sebagai Intervensi Pendidikan di Indonesia,” J. Kiprah Pendidik., vol. 4, no. 3, pp. 362–368, 2025, doi: 10.33578/kpd.v4i3.p362-368.
[7] R. Widaryanti, Casnuri, dan Metty, “Penurunan Masalah Gizi Pada Anak Usia Dini Melalui Edukasi PMT-AS,” Dinamisia, vol. 6, no. 5, pp. 1168–1173, 2022, doi: 10.31849/dinamisia.v6i5.10762.
[8] K. L. Tan, C. P. Lee, dan K. M. Lim, “A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research,” Appl. Sci., vol. 13, no. 7, Art. no. 4550, 2023, doi: 10.3390/app13074550.
[9] M. AminiMotlagh, H. Shahhoseini, dan N. Fatehi, “A reliable sentiment analysis for classification of tweets in social networks,” Soc. Netw. Anal. Min., vol. 13, no. 1, Art. no. 7, 2023, doi: 10.1007/s13278-022-00998-2.
[10] S. Styawati, A. R. Isnain, N. Hendrastuty, dan L. Andraini, “Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis,” J. Inform. J. Pengemb. IT, vol. 6, no. 1, pp. 56–60, 2021, doi: 10.30591/jpit.v6i1.3245.
[11] R. L. Mustofa dan B. Prasetiyo, “Sentiment analysis using lexicon-based method with naive bayes classifier algorithm on #newnormal hashtag in twitter,” J. Phys. Conf. Ser., vol. 1918, no. 4, Art. no. 042155, 2021, doi: 10.1088/1742-6596/1918/4/042155.
[12] S. K. Assayed, K. Shaalan, M. Alkhatib, dan S. Maghaydah, “Machine Learning ChatBot for Sentiment Analysis of COVID-19 Tweets,” in Comput. Sci. Inf. Technol., vol. 13, 2023, pp. 41–55, doi: 10.5121/csit.2023.130404.
[13] C.-H. Lin dan U. Nuha, “Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy,” J. Big Data, vol. 10, Art. no. 88, 2023, doi: 10.1186/s40537-023-00782-9.
[14] H. Imaduddin, F. Y. A’la, dan Y. S. Nugroho, “Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 8, pp. 113–117, 2023, doi: 10.14569/IJACSA.2023.0140813.
[15] H. Ahmadian, T. F. Abidin, H. Riza, dan K. Muchtar, “Transformer-Based Indonesian Language Model for Emotion Classification and Sentiment Analysis,” in Proc. 2023 IEEE Int. Conf. Inf. Technol. Comput. (ICITCOM), 2023, pp. 209–214, doi: 10.1109/ICITCOM60176.2023.10442970.
[16] R. Pramana, M. Jonathan, H. S. Yani, and R. Sutoyo, “A comparison of BiLSTM, BERT, and ensemble method for emotion recognition on Indonesian product reviews,” Procedia Comput. Sci., vol. 245, pp. 399–408, 2024, doi: 10.1016/j.procs.2024.10.266.
[17] U. K. Das, R. S. Ani, N. Datta, I. Fahad, J. Sikder, dan U. Sara, “Enhancing sentiment analysis accuracy on social media comments using a tuned BERT model,” Discov. Comput., vol. 28, Art. no. 198, 2025, doi: 10.1007/s10791-025-09599-x.
[18] A. Maretta dan A. Meiriza, “Aspect-Based Sentiment Analysis of Hospital Service Reviews Using Fine-Tuned IndoBERT,” J. Appl. Inform. Comput., vol. 9, no. 5, pp. 2541–2551, 2025, doi: 10.30871/jaic.v9i5.10765.
[19] H. Murfi, Syamsyuriani, T. Gowandi, G. Ardaneswari, dan S. Nurrohmah, “BERT-Based Combination of Convolutional and Recurrent Neural Network for Indonesian Sentiment Analysis,” Appl. Soft Comput., vol. 151, Art. no. 111112, 2024, doi: 10.1016/j.asoc.2023.111112.
[20] M. Fuadi, A. D. Wibawa, dan S. Sumpeno, “idT5: Indonesian Version of Multilingual T5 Transformer,” arXiv:2302.00856, 2023, doi: 10.48550/arXiv.2302.00856.
[21] K. Kucher, C. Paradis, dan A. Kerren, “The state of the art in sentiment visualization,” Comput. Graph. Forum, vol. 37, no. 1, pp. 71–96, 2018, doi: 10.1111/cgf.13217.
[22] J. Tao dan X. Fang, “Toward multi-label sentiment analysis: A transfer learning based approach,” J. Big Data, vol. 7, Art. no. 1, 2020, doi: 10.1186/s40537-019-0278-0.
[23] S. Ahmad, S. M. Saqib, A. H. Syed, N. Alromema, dan A. Kararay, “Exploring the best fit: A comparative analysis of AFINN, TextBlob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction,” Mehran Univ. Res. J. Eng. Technol., vol. 44, no. 2, pp. 197–216, 2025, doi: 10.22581/muet1982.3449.
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