Analisis Sentimen Publik Program Makan  Bergizi Gratis (MBG) di Youtube:  Perbandingan Kinerja Algoritma Support  Vector Machine (SVM) dan Random Forest

Ratna Yulika Go; Hadasa Sarah Divanda Gaspersz; R. Hendra Kusumawardhana; Nur Aeni Hidayah

doi:10.35960/ikomti.v7i1.2424

Authors

Ratna Yulika Go Universitas Esa Unggul
Hadasa Sarah Divanda Gaspersz Universitas Esa Unggul
R. Hendra Kusumawardhana Universitas Indonesia
Nur Aeni Hidayah Universitas Islam Negeri Syarif Hidayatullah

DOI:

https://doi.org/10.35960/ikomti.v7i1.2424

Keywords:

Sentiment Analysis, Free Nutritious Meals, Support Vector Machine, Random Forest

Abstract

Public attention toward the Free Nutritious Meal Program (Program Makan Bergizi Gratis/MBG) has increased following mass food poisoning incidents in various regions. Data from the National Nutrition Agency (Badan Gizi Nasional/ BGN) up to September 2025 recorded 6,517 affected students, while the Indonesian Education Monitoring Network (Jaringan Pemantau Pendidikan Indonesia/JPPI) reported 11,566 victims as of October 12, 2025. This situation triggered widespread discussion on social media, with YouTube serving as the main platform for public opinion. The large number of unstructured comments rendered manual analysis ineffective, necessitating the use of automated analytical methods. This study aimed to analyze public sentiment toward the MBG program and compare the performances of the Support Vector Machine (SVM) and Random Forest algorithms. This study adopts a quantitative approach using 7,522 YouTube comments as the initial dataset, which are processed through data collection, text preprocessing, data labeling, and TF-IDF feature extraction stages. Subsequently, the SVM and Random Forest models were trained and tested to classify sentiments into positive, negative, and neutral categories. The model performance was evaluated using Accuracy, Precision, Recall, and F1-Score metrics. After the preprocessing stage, the number of data points used in the sentiment analysis decreased compared to the initial dataset. This reduction resulted from data filtering, in which irrelevant, duplicate, or sentiment-unclear data were removed from the dataset. The final cleaned dataset consisted of 7,030 YouTube comments regarding the Free Nutritious Meal Program (MBG). The results show that public opinion is dominated by neutral sentiment (77.17% or 5,425 data points), followed by negative sentiment (15.21% or 1,069 data points), which proportionally exceeds positive sentiment (7.62% or 536 data points). In the modeling stage, the Support Vector Machine (SVM) algorithm proved to be more robust and effective in handling imbalanced data than Random Forest. However, the Random Forest achieved a slightly higher accuracy (83.42% compared to 82.71%).

References

[1] R. Qomarrullah, S. Suratni, L. W. S, and M. Sawir, “Dampak Jangka Panjang Program Makan Bergizi Gratis terhadap Kesehatan dan Keberlanjutan Pendidikan,” Indonesian Journal of Intellectual Publication, vol. 5, no. 2, Mar. 2025, doi: 10.51577/ijipublication.v5i2.660.

[2] N. N. P. Cahyati, “A comprehensive study on MBG (Makan Bergizi Gratis) in the Prabowo-Gibran cabinet: Evaluating the psychological and health impacts of the policy,” Sikontan Journal, vol. 3, no. 4, pp. 177–186, 2025.

[3] F. A. Suprapto, E. Praditya, R. M. Dewi, and W. Adiyoso, “A policy implementation review of the free nutritious meal (MBG) program,” J. Indones. Sustain. Dev. Plan., vol. 6, no. 2, pp. 297–312, 2025, doi: 10.46456/jisdep.v6i2.798.

[4] Office of Assistant to Deputy Cabinet Secretary for State Documents & Translation, “President Prabowo: Free Nutritious Meal Program is Investment for Nation’s Future,” Cabinet Secretariat of The Republic of Indonesia, 2025. https://setkab.go.id/en/president-prabowo-free-nutritious-meal-program-is-investment-for-nations-future/ (accessed Oct. 14, 2025).

[5] J. Carina, “Daftar 56 Dapur MBG yang Dinonaktifkan BGN imbas Kasus Keracunan,” Kompas.com, 2025. https://nasional.kompas.com/read/2025/09/30/00034261/daftar-56-dapur-mbg-yang-dinonaktifkan-bgn-imbas-kasus-keracunan (accessed Oct. 22, 2025).

[6] Idris Rusadi Putra, “Data BGN: 6.517 Siswa Keracunan MBG Selama Januari-September 2025,” merdeka.com, 2025. https://www.merdeka.com/peristiwa/data-bgn-6517-siswa-keracunan-mbg-selama-januari-september-2025-475384-mvk.html/ (accessed Oct. 14, 2025).

[7] Ervana Trikarinaputri, “JPPI: Korban Keracunan MBG hingga 12 Oktober Capai 11 Ribu Orang,” Tempo, 2025. https://www.tempo.co/politik/jppi-korban-keracunan-mbg-hingga-12-oktober-capai-11-ribu-orang-2078963 (accessed Oct. 14, 2025).

[8] O. El Azzouzy, T. Chanyour, and S. J. Andaloussi, “Transformer-based models for sentiment analysis of YouTube video comments,” Sci. African, vol. 29, p. e02836, 2025, doi: 10.1016/j.sciaf.2025.e02836.

[9] A. Virani, B. Nagra, J. O’Mahony, J. Bacsu, J. K. Ghatore, and S. Panda, “Using YouTube Comments Data to Explore Postpartum Depression in Social Media: An Infodemiology Study,” Int. J. Environ. Res. Public Health, vol. 21, no. 11, pp. 1–20, 2024, doi: 10.3390/ijerph21111526.

[10] M. Tsiourlini, K. Tzafilkou, D. Karapiperis, and C. Tjortjis, “Text Analytics on YouTube Comments for Food Products,” Inf., vol. 15, no. 10, 2024, doi: 10.3390/info15100599.

[11] I. Chalkias, K. Tzafilkou, D. Karapiperis, and C. Tjortjis, “Learning Analytics on YouTube Educational Videos: Exploring Sentiment Analysis Methods and Topic Clustering,” Electron., vol. 12, no. 18, 2023, doi: 10.3390/electronics12183949.

[12] L. Ashbaugh and Y. Zhang, “A Comparative Study of Sentiment Analysis on Customer Reviews Using Machine Learning and Deep Learning,” Computers, vol. 13, no. 12, 2024, doi: 10.3390/computers13120340.

[13] A. B. Alawi and F. Bozkurt, “A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data,” Decis. Anal. J., vol. 11, no. December 2023, p. 100473, 2024, doi: 10.1016/j.dajour.2024.100473.

[14] F. Fauzi et al., “Analisis sentimen pengguna YouTube terhadap Program Vaksin COVID-19,” pp. 141–148, 2021.

[15] Munawar, “Extract Transform Loading (ETL) Based Data Quality for Data Warehouse Development,” Proc. 2021 1st Int. Conf. Comput. Sci. Artif. Intell. ICCSAI 2021, no. October, pp. 373–378, 2021, doi: 10.1109/ICCSAI53272.2021.9609770.

[16] A. Daza, N. D. González Rueda, M. S. Aguilar Sánchez, W. F. Robles Espíritu, and M. E. Chauca Quiñones, “Sentiment Analysis on E-Commerce Product Reviews Using Machine Learning and Deep Learning Algorithms: A Bibliometric Analysisand Systematic Literature Review, Challenges and Future Works,” Int. J. Inf. Manag. Data Insights, vol. 4, no. 2, 2024, doi: 10.1016/j.jjimei.2024.100267.

[17] R. D. Cahyani and P. T. Prasetyaningrum, “Sentiment Analysis of User Reviews for AI Applications: Evaluating SVM, Logistic Regression, and Random Forest,” Journal of Information Systems and Informatics, vol. 8, no. 1, pp. 1–27, 2026, doi: 10.63158/journalisi.v8i1.1366.

[18] I. Pratistha, A. P. S. Iskandar, E. G. R. Lanang, and N. W. J. K. Dewi, “Comparison of Random Forest and SVM methods in sentiment analysis about electric cars in Indonesia,” Jurnal Mandiri IT, vol. 14, no. 1, pp. 29–36, 2025, doi: 10.35335/mandiri.v14i1.424.

[19] B. J. Azzahra, H. A. Id, and K. Agus, “Analisis sentiment terhadap pengguna mobil listrik berdasarkan komentar YouTube menggunakan metode SVM dan teknik ensemble,” Jurnal Pendidikan Informatika dan Sains, vol. 14, no. 1, pp. 10–23, 2025, doi: 10.31571/saintek.v14i1.8871.

[20] J. R. Dani and P. Parjito, “Analisis Sentimen Komentar YouTube terhadap Kenaikan Tunjangan DPR RI menggunakan Naïve Bayes, SVM, dan Random Forest,” Building of Informatics, Technology and Science (BITS), vol. 7, no. 3, pp. 1512–1524, 2025, doi: 10.47065/bits.v7i3.8513.

[21] L. Septiana, V. Yasin, and A. Z. Sianipar, “Analyzing public sentiment on YouTube comments regarding the free lunch policy using the Support Vector Machine (SVM) algorithm,” Jurnal Mandiri IT, vol. 14, no. 1, pp. 1–10, 2024, doi: 10.35335/mandiri.v14i1.434.

[22] T. A. Khan, R. Sadiq, Z. Shahid, M. M. Alam, and M. B. M. Su’ud, “Sentiment analysis using Support Vector Machine and Random Forest,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 45–58, 2024, doi: 10.33093/jiwe.2024.3.1.5.

[23] S. Khomsah, “Sentiment Analysis on YouTube Comments Using Word2Vec and Random Forest,” Telematika: Jurnal Informatika dan Teknologi Informasi, vol. 18, no. 1, pp. 61–72, 2021, doi: 10.31315/telematika.v18i1.4493.

[24] B. A. Santoso, B. I. Nugroho, and D. U. Asyfiya, “Perbandingan algoritma Naïve Bayes, SVM, dan Random Forest untuk analisis sentimen komentar politik YouTube,” TIN: Terapan Informatika Nusantara, vol. 6, no. 4, pp. 320–331, 2025, doi: 10.47065/tin.v6i4.8326.

[25] E. D. Dewantoro and S. Rakasiwi, “Analisis komparatif kinerja SVM, Random Forest, dan Naïve Bayes untuk klasifikasi sentimen pada komentar YouTube,” BITS Journal, vol. 7, no. 4, pp. 1603–1610, 2025, doi: 10.47065/bits.v7i4.8959.

[26] B. K. Syafia, M. F. Hidayattullah, and W. Suteddy, “Studi komparasi algoritma SVM dan Random Forest pada analisis sentimen komentar YouTube BTS,” Jurnal Informatika, vol. 14, no. 2, pp. 85–94, 2021, doi: 10.30646/sinus.v18i2.457.