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All Journal TEKNIK INFORMATIKA Prosiding Semnastek Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Building of Informatics, Technology and Science Oryza: Jurnal Agribisnis dan Pertanian Berkelanjutan Prosiding Seminar Nasional Inovasi Teknologi Terapan Jurnal INFOTEL Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, dan Humaniora Fajar: Media Komunikasi dan Informasi Pengabdian Kepada Masyarakat
Muhamad Azhari
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Religion Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Other

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Journal : Building of Informatics, Technology and Science

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Analisis Sentimen Opini Publik Program Makan Siang Gratis dengan Random Forest Pada Media Azhari, Muhamad; Parjito, Parjito
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6423

Abstract

The "Free Lunch Program," introduced as part of the 2024 Indonesian election campaign, became a hot topic on social media, especially on the platform X. This program aims to improve children's health and nutrition while reducing stunting rates by providing free lunches and milk to children and pregnant women. A study was conducted to analyze public sentiment regarding the program using the Random Forest algorithm. The data consisted of 9,347 tweets collected through web crawling. The analysis revealed that the majority of sentiments were negative (8,021 entries), while positive sentiments accounted for only 430 entries. The preprocessing steps included data cleaning, case folding, tokenization, stopword removal, and stemming. The imbalance between positive and negative sentiment data was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), resulting in a more balanced dataset. After applying SMOTE, the model achieved 100% accuracy, with significant improvements in precision, recall, and F1-Score. The analysis showed that positive sentiments focused on the program's health and educational benefits, while negative sentiments highlighted criticism of implementation and budget allocation. This study demonstrates the value of sentiment analysis in evaluating social programs and understanding public perceptions.
Co-Authors A., Amelia Aan Febriansyah Andika Noviawan, Lalu Arini Arini AW., Sawung Dedy Cahyadi, Dedy Eko Sulistyo Fahrul Agus Feri Fahrianto Fitri, Isnaieni Ijtima’ Amna Hendra Bayu Suseno Karina Zaruska Karin Khaerul Umam, Khaerul krisma, alviani kusnandar kusnandar Paradisa Sukma Parjito, Parjito Pratiwi, Anggy Eka Putut Pamilih Widagdo, Putut Pamilih Ramadiani - Rispawati, Dewi S., Wardana Shofi, Imam Marzuki Victor Amrizal Viva Arifin Widodo, Tunggal