Sentiment Analysis of Free Nutritious Meal Program on Platform X: Comparing Naive Bayes, SVM, Random Forest, and IndoBERT
DOI:
https://doi.org/10.30871/jaic.v10i3.12959Keywords:
Sentiment Analysis, TF-IDF, MBG Program, Social MediaAbstract
The Free Nutritious Meal Program (MBG), launched by the Indonesian government in January 2025, generated various public responses on social media, particularly on platform X. This study aims to analyze public sentiment toward the MBG Program and compare the performance of four sentiment classification methods: Naive Bayes, Support Vector Machine (SVM), Random Forest, and IndoBERT. The dataset was collected through tweet crawling using the keywords “MBG” and “Makan Bergizi Gratis” during the period of July–December 2025, resulting in 1,906 Indonesian-language tweets. The preprocessing stage included cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed using the InSet Lexicon and produced 1,113 negative tweets and 793 positive tweets. Manual validation on part of the dataset was conducted by two independent annotators and achieved a Cohen’s Kappa score of 0.78, indicating substantial agreement. For classical machine learning models, feature extraction was carried out using TF-IDF, while IndoBERT used contextual text representations without stemming. Class imbalance in classical models was handled using SMOTE, whereas IndoBERT applied class weighting. The experimental results show that IndoBERT achieved the best performance with an accuracy of 92.93%. Among the classical models, SVM produced the highest performance with an accuracy of 92.15%, followed by Naive Bayes and Random Forest. Word frequency analysis also revealed that positive sentiment was mainly associated with support for the program and nutrition-related topics, while negative sentiment was dominated by concerns about food safety, budget management, and criticism of the program. Based on the findings, IndoBERT is more effective in understanding the context of Indonesian-language tweets. However, TF-IDF-based classical models, especially SVM, still provide competitive performance with lower computational requirements, making them suitable for sentiment analysis in public policy studies.
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[1] E. M. Sipayung, “Sentiment on Public Trust Using the NLP Rule Based Method,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 12, no. 1, p. 175, 2024, doi: 10.26418/justin.v12i1.72426.
[2] A. M. Rizqiyah and I. K. D. Nuryana, “Analisis Sentimen Masyarakat terhadap Kebijakan Iuran Tabungan Perumahan Rakyat (Tapera) pada Platform X Menggunakan Algoritma Naïve Bayes Classifier dan Support Vector Machine,” Journal of Emerging Information Systems and Business Intelligence (JEISBI), vol. 5, no. 3, pp. 298–306, 2024, [Online]. Available: https://ejournal.unesa.ac.id/index.php/JEISBI/article/view/64074
[3] F. Fathoni, A. Ibrahim, F. Rizka Mumtaz, M. Azmi Zaky, M. Jodi Pratama, and I. Akbar Kurniawan, “Analisis Sentimen Public Twitter Terhadap Kebijakan Pemerintah Menggunakan Metode Svm (Studi Kasus : Ruu Tni ),” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 4, pp. 6322–6329, 2025, doi: 10.36040/jati.v9i4.14036.
[4] L. S. Memory et al., “SMATIKA : STIKI Informatika Jurnal Sentiment analysis on public opinion trends on # MakanBergiziGratis programs on,” vol. 16, no. 79, pp. 175–189, 2026.
[5] L. Nursinggah, R. Ruuhwan, and T. Mufizar, “Analisis Sentimen Pengguna Aplikasi X Terhadap Program Makan Siang Gratis Dengan Metode Naïve Bayes Classifier,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, 2024, doi: 10.23960/jitet.v12i3.4336.
[6] N. N. Aprianti, N. M. M. R. Desmayani, L. G. B. Libraeni, I. G. A. Indrawan, and M. L. Radhitya, “Public Sentiment Analysis of the Free Nutritious Meals Program (MBG) on Social Media X Using the Naive Bayes Method,” Journal of Applied Informatics and Computing, vol. 9, no. 6, pp. 3929–3936, 2025, doi: 10.30871/jaic.v9i6.11420.
[7] M. F. Saleh and R. Imanda, “Public Sentiment Analysis of the Free Meal Program: A Comparison of Naive Bayes and Support Vector Machine Methods on the Twitter (X) Social Media Platform,” Journal of Applied Informatics and Computing, vol. 9, no. 1, pp. 131–139, 2025, doi: 10.30871/jaic.v9i1.8895.
[8] A. Sitanggang, Y. Umaidah, Y. Umaidah, R. I. Adam, and R. I. Adam, “Analisis Sentimen Masyarakat Terhadap Program Makan Siang Gratis Pada Media Sosial X Menggunakan Algoritma Naïve Bayes,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, 2024, doi: 10.23960/jitet.v12i3.4902.
[9] Z. Purwanti and Sugiyono, “Pemodelan Text Mining untuk Analisis Sentimen Terhadap Program Makan Siang Gratis di Media Sosial X Menggunakan Algoritma Support Vector Machine (SVM),” Jurnal Indonesia : Manajemen Informatika dan Komunikasi, vol. 5, no. 3, pp. 3065–3079, 2024, doi: 10.35870/jimik.v5i3.1001.
[10] N. Nur, A. Aryanti, and O. Suria, “Analisis Sentimen Terhadap Pemutusan Hubungan Kerja Di Indonesia : Komparasi Indobert Dengan Svm , Random Forest , Dan Decision Tree Dengan Optimasi Tf - Idf Pendahuluan Pemutusan Hubungan Kerja ( PHK ) merupakan salah satu fenomena sosial dan ekonomi yan,” vol. 10, no. 2, pp. 1158–1176, 2025.
[11] R. Rahmadani, A. Rahim, and R. Rudiman, “Analisis Sentimen Ulasan ‘Ojol the Game’ Di Google Play Store Menggunakan Algoritma Naive Bayes Dan Model Ekstraksi Fitur Tf-Idf Untuk Meningkatkan Kualitas Game,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, 2024, doi: 10.23960/jitet.v12i3.4988.
[12] A. Winston, N. D. Rafael, and H. Lucky, “Analyzing Public Sentiment Toward the Makan Bergizi Gratis ( MBG ) Program on TikTok Using SVM and IndoBERT,” vol. 3, no. 1, pp. 33–39, 2026, doi: 10.21512/ijcshaijournal.v3i1.15184.
[13] J. Homepage, U. Arfan, and E. Badii, “MALCOM: Indonesian Journal of Machine Learning and Computer Science Sentiment Analysis of Papuan Public Perceptions Toward the Free Nutritional Meal Program in Indonesia Analisis Sentimen Masyarakat Papua Terhadap Program Makan Gizi Gratis di Indonesia,” vol. 5, no. October, pp. 1538–1546, 2025.
[14] V. B. Lestari and C. A. Hutagalung, “Evaluation of TF-IDF Extraction Techniques in Sentiment Analysis of Indonesian-Language Marketplaces Using SVM, Logistic Regression, and Naive Bayes,” Journal of Computer Science and Applications, vol. 8, no. 1, pp. 22–2025, 2025, [Online]. Available: https://doi.org/10.21009/j-
[15] N. Husin, “Komparasi Algoritma Random Forest, Naïve Bayes, dan Bert Untuk Multi-Class Classification Pada Artikel Cable News Network (CNN),” Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer, vol. 7, no. 1, pp. 75–84, 2023, doi: 10.55886/infokom.v7i1.608.
[16] Heti Aprilianti, Khothibul Umam, and Maya Rini Handayani, “Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews,” Journal of Applied Informatics and Computing, vol. 9, no. 3, pp. 920–927, 2025, doi: 10.30871/jaic.v9i3.9482.
[17] H. Jayadianti, W. Kaswidjanti, A. T. Utomo, S. Saifullah, F. A. Dwiyanto, and R. Drezewski, “Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN,” ILKOM Jurnal Ilmiah, vol. 14, no. 3, pp. 348–354, 2022, doi: 10.33096/ilkom.v14i3.1505.348-354.
[18] M. Samantri and Afiyati, “Perbandingan Algoritma Support Vector Machine dan Random Forest untuk Analisis Sentimen Terhadap Kebijakan Pemerintah Indonesia Terkait Kenaikan Harga BBM Tahun 2022,” Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 8, no. 1, pp. 1–9, 2024, doi: 10.35870/jtik.v8i1.1202.
[19] N. S. Sediatmoko, Y. Nataliani, and I. Suryady, “Sentiment Analysis of Customer Review Using Classification Algorithms and SMOTE for Handling Imbalanced Class,” Indonesian Journal of Information Systems, vol. 7, no. 1, pp. 38–52, 2024, doi: 10.24002/ijis.v7i1.8879.
[20] M. F. As Shidiq and D. Alita, “Analisis Sentimen Masyarakat Terhadap Kasus Judi Online Menggunakan Data Dari Media Sosial X Pendekatan Naive Bayes Dan Svm,” Jurnal Sistem Informasi dan Informatika (Simika), vol. 8, no. 1, pp. 24–35, 2025, doi: 10.47080/simika.v8i1.3624.
[21] B. F. S. Supriyanto and S. Rosalin, “Analisis Sentimen Program Merdeka Belajar dengan Text Analysis Wordcloud & Word Frequency,” Jurnal Minfo Polgan, vol. 12, no. 1, pp. 25–32, 2023, doi: 10.33395/jmp.v12i1.12312.
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