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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Information Systems and Informatics RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Teknika Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal INFOTEL RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
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An Integrated Random Forest for Analyzing Public Sentiment on the “Makan Bergizi Gratis” Program Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1184

Abstract

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.