Widyawati, Anisa and -, Endang Wahyu Pamungkas, S.Kom, M.Kom. (2026) Analisis Sentimen Publik Terhadap Isu Tunjangan Anggota Dpr Pada Media Sosial X Menggunakan Algoritma Machine Learning Tradisional. Skripsi thesis, Universitas Muhammadiyah Surakarta.
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Abstract
The development of digital technology has transformed how society communicates and participates in public discourse, particularly through social media platforms like X (Twitter). This platform has become a vital data source for understanding public opinion and sentiment toward various social and political issues. One widely debated issue is the 2025 housing allowance policy for members of the House of Representatives (DPR), which went viral following reports of a Rp50 million monthly housing subsidy. Public reactions to this issue were diverse, ranging from support to strong opposition, expressed through various posts and hashtags such as #TolakTunjanganDPR and #DPRPuasRakyatCemas. However, these opinions are unstructured, making objective analysis difficult. Therefore, this study aims to analyze public sentiment regarding the DPR allowance issue using the Naïve Bayes Classifier and Support Vector Machine (SVM) algorithms. These two methods are compared based on their ability to classify Indonesian-language tweets into positive, negative, and neutral categories. The dataset consists of 2,012 opinions extracted using TF-IDF with an 80:20 training and testing split. The findings indicate that SVM achieved the highest performance, with an accuracy of 80.4%, precision of 79.5%, recall of 80.4%, and an F1-score of 78.23%. This study offers a comprehensive understanding of public perception regarding DPR policies and has the potential to serve as a foundation for developing a real-time sentiment monitoring system applicable to various national policy issues.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Sentiment Analysis, Naïve Bayes Classifier, Support Vector Machine, Twitter, DPR Members' Allowances, Machine Learning |
| Subjects: | T Technology > Information Technology > Artificial Intelligence T Technology > Information Technology > Software. Aplication > Software Engineering |
| Divisions: | Fakultas Komunikasi dan Informatika > S1 Teknik Informatika |
| Depositing User: | ANISA WIDYAWATI |
| Date Deposited: | 14 May 2026 13:50 |
| Last Modified: | 14 May 2026 13:50 |
| URI: | http://eprints.ums.ac.id/id/eprint/145255 |
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