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), 2025)
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.