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All Journal International Journal of Artificial Intelligence Research JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Jusikom: Jurnal Sistem Informasi Ilmu Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) Abdi Kami : Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research Jurnal Algoritma
Wahiddin, Deden
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : Building of Informatics, Technology and Science

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Analisis Sentimen Rencana Penerapan Cukai Pada Minuman Manis Kemasan Menggunakan Algoritma Naive Bayes dan Logistic Regression Gozali, Gozali; Baihaqi, Kiki Ahmad; Sukmawati, Cici Emilia; Wahiddin, Deden
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7411

Abstract

The plan to impose excise tax on packaged sweetened beverages (PSB) is proposed as a strategic measure to reduce sugar consumption among the public. This policy has elicited various responses from society, especially on social media platforms such as TikTok. The purpose of this study is to evaluate public sentiment towards the PSB excise tax policy by analyzing comments posted on the TikTok platform, comparing the performance of the Naive Bayes and Logistic Regression algorithms. Data were collected from comments on news videos about the implementation of the excise tax on PSB posted by official journalist accounts on TikTok, using the TikTok Comments Scraper available on the apipy website, resulting in 1,332 comments. The data were processed through preprocessing steps including text cleaning, tokenization, stemming, and word weighting using TF-IDF. After expert sentiment labeling, the data were then split into training and testing sets with an 80:20 ratio. Evaluation was conducted using a confusion matrix to obtain performance metrics such as accuracy, precision, recall, and F1-score for each model. The analysis revealed that negative comments dominated at 65.2%, while positive comments accounted for 34.8%. The Logistic Regression algorithm achieved an accuracy of 81.37%, precision of 86.22%, recall of 75.14%, and an F1-score of 77.06%. Meanwhile, the Naive Bayes algorithm obtained an accuracy of 79.85%, precision of 82.19%, recall of 74.17%, and an F1-score of 75.76%. It can be concluded that the majority of TikTok users still express negative responses to the PSB excise tax policy, and the Logistic Regression algorithm demonstrates superior performance in sentiment classification compared to the Naive Bayes algorithm.
Co-Authors AA Sudharmawan, AA Abdul Gapur Ajie, Prasetyo April Lia Hananto Awal, Elsa Elvira Baihaqi, Kiki Ahmad Enjelia, Lola Faisal, Sutan Fauzi, Ahmad Fauzi, Farras Ahmad Fauzi, Rifqi Arul Fitri Nur Masruriyah, Anis Gozali, Gozali Halif, Jenny Hanny Hikmayanti Handayani Hartini, Dina Hastari, Nazwa Putri Iman Sanjaya Indra, Jamaludin Juwita, Ayu Ratna Lestari, Santi Arum Puspita Mudzakir, Tohirin Al Novita, Hilda Yulia Nurlaelasari, Euis Nurmayanti, Trisya Nursyawalni, Reva Pratiwi, Gita Risky Rahmat Ramadhan, Muchamad Fachrul Ramadhan, Rizky Agung Ridho Pratama, Ilham Rohana, Tatang Sukmawati, Cici Emilia Yana Cahyana