An Integrated Random Forest for Analyzing Public Sentiment on the “Makan Bergizi Gratis” Program
DOI:
https://doi.org/10.51519/journalisi.v7i3.1184Keywords:
Analysis Sentiment, Ensemble Learning, Random Forest, MBG, TF-IDFAbstract
The “Makan Bergizi Gratis” (MBG) Program is a public policy aimed at improving the nutritional quality of the community, particularly vulnerable groups. However, the success of this program is heavily influenced by public sentiment and perception. This research analyzes public sentiment toward the MBG program thru the social media platform X using an ensemble-based machine learning approach. The proposed framework integrates the Random Forest algorithm and compares it with four other ensemble models: AdaBoost, XGBoost, Bagging, and Stacking. A total of 3,417 tweets were analyzed using the TF-IDF method, both with and without stemming. The Random Forest model showed the best performance with an accuracy of 91.15% and an ROC-AUC of 95.46% on the data without stemming, consistently outperforming the other models. Additionally, a visual analysis of word frequency provides a strong indication of public opinion. These findings demonstrate the effectiveness of Random Forest in managing unstructured sentiment data and provide valuable insights for policymakers to monitor public responses and improve program implementation with greater precision.
Downloads
References
N. K. Arora and I. Mishra, “United Nations Sustainable Development Goals 2030 and environmental sustainability: race against time,” Environ. Sustain., vol. 2, no. 4, pp. 339–342, Dec. 2019.
J.-G. Shim, K.-H. Ryu, S. H. Lee, E.-A. Cho, Y. J. Lee, and J. H. Ahn, “Text mining approaches to analyze public sentiment changes regarding COVID-19 vaccines on social media in Korea,” Int. J. Environ. Res. Public Health, vol. 18, no. 12, p. 6549, June 2021.
M. E. Basiri, S. Nemati, M. Abdar, S. Asadi, and U. R. Acharrya, “A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets,” Knowl. Based Syst., vol. 228, no. 107242, p. 107242, Sept. 2021.
Z. Jianqiang, G. Xiaolin, and Z. Xuejun, “Deep convolution neural networks for twitter sentiment analysis,” IEEE Access, vol. 6, pp. 23253–23260, 2018.
N. G. Ramadhan, Adiwijaya, W. Maharani, and A. Akbar Gozali, “Chronic diseases prediction using machine learning with data preprocessing handling: a critical review,” IEEE Access, vol. 12, pp. 80698–80730, 2024.
A. Singh, N. Thakur, and A. Sharma, “A review of supervised machine learning algorithms,” in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 1310–1315.
B. Rahmatullah, S. A. Saputra, P. Budiono, and D. P. Wigandi, “Sentimen Analisis Makan Bergizi Gratis Menggunakan Algoritma Naive Bayes,” JIfoTech, vol. 5, no. 1, Mar. 2025.
Elsa Triningsih, “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, Jan. 2025.
W. Anggriyani, “Analisis Sentimen Program Makan Gratis Pada Media Sosial X Menggunakan Metode NLP,” JoSYC: Journal of Computer System and Informatics, vol. 5, no. 4, 2024.
A. Uddin, X. Tao, C.-C. Chou, and D. Yu, “Are missing values important for earnings forecast? a machine learning perspective,” Quant. Finance, vol. 22, no. 6, pp. 1113–1132, Jan. 2022.
Arpita, P. Kumar, and K. Garg, “Data cleaning of raw tweets for sentiment analysis,” in 2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN), Rajpura, Punjab, India, 2020, pp. 273–276.
S. Aldera, A. Emam, M. Al-Qurishi, M. Alrubaian, and A. Alothaim, “Exploratory data analysis and classification of a new Arabic online extremism dataset,” IEEE Access, vol. 9, pp. 161613–161626, 2021.
H. Mohamed Zakir and S. Vinila Jinny, “A comparative study on data cleaning approaches in sentiment analysis,” in Lecture Notes in Electrical Engineering, Singapore: Springer Singapore, 2020, pp. 421–431.
H.-T. Duong and T.-A. Nguyen-Thi, “A review: preprocessing techniques and data augmentation for sentiment analysis,” Comput. Soc. Netw., vol. 8, no. 1, Dec. 2021.
K. Afifah, I. N. Yulita, and I. Sarathan, “Sentiment analysis on telemedicine app reviews using XGBoost classifier,” in 2021 International Conference on Artificial Intelligence and Big Data Analytics, Bandung, Indonesia, 2021, pp. 22–27.
W. Kim, “Comparative study of tokenizer based on learning for sentiment analysis,” Journal of the Korean Society for Quality Management, vol. 48, Sept. 2020.
I. Steinke, J. Wier, L. Simon, and R. I. Seetan, “Sentiment analysis of online movie reviews using machine learning,” Int. J. Adv. Comput. Sci. Appl., 2022.
A. Jabbar, S. Iqbal, M. I. Tamimy, A. Rehman, S. A. Bahaj, and T. Saba, “An analytical analysis of text stemming methodologies in information retrieval and natural language processing systems,” IEEE Access, vol. 11, pp. 133681–133702, 2023.
A. Mee, E. Homapour, F. Chiclana, and O. Engel, “Sentiment analysis using TF–IDF weighting of UK MPs’ tweets on Brexit,” Knowl. Based Syst., vol. 228, no. 107238, p. 107238, Sept. 2021.
V. Balakrishnan, S. Khan, and H. R. Arabnia, “Improving cyberbullying detection using Twitter users’ psychological features and machine learning,” Comput. Secur., vol. 90, no. 101710, p. 101710, Mar. 2020.
N. G. Ramadhan and F. Adhinata, “Sentiment analysis on vaccine COVID-19 using word count and Gaussian Naïve Bayes,” Indones. J. Electr. Eng. Comput. Sci., June 2022.
M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artif. Intell. Rev., vol. 55, no. 7, pp. 5731–5780, Oct. 2022.
W. Zhang, X. Li, Y. Deng, L. Bing, and W. Lam, “A survey on aspect-based sentiment analysis: Tasks, methods, and challenges,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 11, pp. 11019–11038, Nov. 2023.
S. Sharma, “SentiNet: A Word-Cloud based approach towards Social Media Sentiment Analysis,” in 2024 IEEE International Conference on Contemporary Computing and Communications (InC4), Bangalore, India, 2024, vol. 821, pp. 1–6.
A. Verma and R. Vashisth, “Sentiment analysis of Google play and app store reviews of threads,” in 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2024, pp. 1–5.
Downloads
Published
Issue
Section
License
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
