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All Journal CESS (Journal of Computer Engineering, System and Science) Electro Luceat Jurnal Penelitian Pendidikan IPA (JPPIPA) JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Teknologi dan Sistem Tertanam Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Infrastruktur Teknologi Informasi Journal Social Science And Technology For Community Service jimel
Sampurna Dadi Riskiono
Universitas Teknokrat Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Physics

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Design and Implementation of a Public Sentiment Prediction Framework on Budget Efficiency Policy using Support Vector Machine, Naïve Bayes, and Random Forest M Sahyudi; Sampurna Dadi Riskiono; Ryan Randy Suryono
Jurnal Penelitian Pendidikan IPA Vol 11 No 12 (2025): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i12.13561

Abstract

Public sentiment toward government budget efficiency policies has become increasingly visible through social media platforms, where citizens actively express opinions, support, and criticism. This study aims to analyze public sentiment toward budget efficiency policies using data collected from the social media platform X (formerly Twitter). A total of 2,000 public comments related to budget efficiency policies were collected through web scraping using the X API. The data were preprocessed through normalization, case folding, text cleaning, tokenization, stopword removal, and stemming. Sentiment classification was conducted using three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Random Forest. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results indicate that SVM achieved the highest accuracy, while Random Forest demonstrated superior recall in identifying positive sentiment. These findings suggest that Random Forest is particularly suitable for sentiment analysis tasks where minimizing false negatives is important, while SVM performs well in overall classification accuracy. This research contributes to the comparative evaluation of machine learning models for public sentiment analysis on policy-related issues using social media data.  
Co-Authors Agung Prasetyo Andi Nurkholis Arinda Rifaini Bintang Diwangkara Elin Gusbriana Fikri Hamidy I Wayan Dexa Alvino Jupriyadi, Jupriyadi Kristianto Kristianto M Sahyudi Nita Ayunandita Rikendry Rikendry Roy Harry Syidiq Pamungkas Rut Dias Valentin Ryan Randy Suryono S. Samsugi surya prambudi Tami Ulfia Try Susanto Yudha Arya Yuri Rahmanto