Sentiment Analysis of Indonesia’s Free Nutritious Meal Program on X Using SVM and Random Forest

Ferdy Aliansyah Hasyim, Talenta Parfaibya Mahenindra, Lilis Sriwahyuni, Alika Azka Shapira, Wigawijayanti Wigawijayanti, Nadhifa Zahra Ghaisani, Mirlan Sujana, Sri Nurdiati, Mohamad Khoirun Najib

Abstract


The Free Nutritious Meal (Makan Bergizi Gratis/MBG) Program was introduced to address stunting in Indonesia, yet its implementation has sparked diverse public debate. This study aims to map public perception on social media X and compare the performance of Support Vector Machine (SVM) and Random Forest algorithms in sentiment classification. Utilizing a large-scale dataset of 7,452 tweets collected via stratified random sampling from January to October 2025, this research applies TF-IDF feature extraction and SMOTE data balancing. The analysis reveals that positive sentiment dominates at 47.62%, while negative sentiment accounts for 39.8\%, and neutral for 12.57%. In model comparison, SVM without SMOTE achieved the best performance with 80.66% accuracy and an F1-Score of 79.79%, outperforming Random Forest, which only reached a maximum accuracy of 72.23% after SMOTE application. These findings provide an objective overview of MBG policy acceptance and methodological insights into the effectiveness of SVM in handling high-dimensional text data.

Keywords


Makan Bergizi Gratis; Random Forest; Sentiment Analysis; Social Media X; Support Vector Machine

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References


[1] I. L. Pramesthi et al., “Evaluating the impact of indonesia’s national school feeding program (progas) on children’s nutrition and learning environment: A mixed-methods approach,” Nutrients, vol. 17, no. 22, p. 3575, 2025. doi: 10.3390/nu17223575

[2] P. M. Putri, A. S. Shafira, and G. S. Mahardhika, “Stunting reduction strategy in indonesia: Maternal knowledge aspects,” The Indonesian Journal of Public Health, vol. 19, no. 2, pp. 329–343, 2024. doi: 10.20473/ijph.v19i2.2024.329-343

[3] F. A. Suprapto, E. Praditya, R. M. Dewi, and W. Adiyoso, “A policy implementation review of the free nutritious meal (mbg) program,” The Journal of Indonesia Sustainable Development Planning, vol. 6, no. 2, pp. 297–312, Aug. 2025. doi: 10.46456/jisdep.v6i2.798

[4] T. Purnomo 341 and W. H. Pamungkas, “The controversy of the free nutritious meal (mbg) program: Food poisoning cases and legal remedies,” Jurnal Humaniora, vol. 9, no. 2, pp. 457–464, 2025. doi: 10.30601/humaniora.v9i2.7391

[5] B. Liu, Sentiment Analysis and Opinion Mining (Synthesis Lectures on Human Language Technologies 1). San Rafael, CA: Morgan & Claypool Publishers, 2012, vol. 5. doi: 10.1007/978-3-031-02145-9

[6] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. doi: 10.1007/BF00994018

[7] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. doi: 10.1023/A:1010933404324

[8] F. Fatkhurrohman, B. I. Nugroho, and N. Fadillah, “Analisis sentimen program makan bergizi gratis pemerintah ri melalui twitter menggunakan metode svm,” RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 3, pp. 3906–3917, Aug. 2025. doi: 10.31004/riggs.v4i3.2533

[9] E. Triningsih, M. Afdal, I. Permana, and N. E. Rozanda, “Analisis sentimen terhadap program makan bergizi gratis menggunakan algoritma machine learning pada sosial media x,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 4, pp. 2240–2250, Mar. 2025. doi: 10.47065/bits.v6i4.6534

[10] M. Napiah, S. Heristian, M. Raharjo, and R. A. Purnama, “Analyzing public sentiment toward makanan bergizi gratis program using machine learning,” Computer Science (CO SCIENCE), vol. 6, no. 1, pp. 30–38, 2026. doi: 10.31294/co-science.v6i1.10445

[11] I. Malashin, V. Tynchenko, A. Gantimurov, V. Nelyub, and A. Borodulin, “Support vector machines in polymer science: A review,” Polymers, vol. 17, no. 4, p. 491, 2025. doi: 10.3390/polym17040491

[12] Y. Restiani and J. Purwadi, “Support vector machine for classification: A mathematical and scientific approach in data analysis,” Jurnal Penelitian Pendidikan IPA, vol. 10, no. 11, pp. 9896–9903, Nov. 2024. doi: 10.29303/jppipa.v10i11.8122

[13] S. Han, B. D. Williamson, and Y. Fong, “Improving random forest predictions in small datasets from two-phase sampling designs,” BMC Medical Informatics and Decision Making, vol. 21, no. 1, p. 322, 2021. doi: 10.1186/s12911-021-01688-3

[14] G.-W. Cha, H.-J. Moon, and Y.-C. Kim, “Comparison of random forest and gradient boosting machine models for predicting demolition waste based on small datasets and categorical variables,” International Journal of Environmental Research and Public Health, vol. 18, no. 16, p. 8530, 2021. doi: 10.3390/ijerph18168530

[15] R. G. Gallager, “Claude e. shannon: A retrospective on his life, work, and impact,” IEEE Transactions on Information Theory, vol. 47, no. 7, pp. 2681–2695, Dec. 2001. doi: 10.1109/18.959253

[16] M. K. Suryadi, R. Herteno, S. W. Saputro, M. R. Faisal, and R. A. Nugroho, “Comparative study of various hyperparameter tuning on random forest classification with smote and feature selection using genetic algorithm in software defect prediction,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 6, no. 2, pp. 137–147, Apr. 2024. doi: 10.35882/jeeemi.v6i2.375

[17] A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 2nd ed. Sebastopol, CA: O’Reilly Media, 2019.

[18] A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics, vol. 17, no. 1, pp. 168–192, 2021. doi: 10.1016/j.aci.2018.08.003

[19] H. Yun, “Prediction model of algal blooms using logistic regression and confusion matrix,” International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 3, pp. 2407–2413, 2021. doi: 10.11591/ijece.v11i3.pp2407-2413




DOI: https://doi.org/10.18860/cauchy.v11i1.40717

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