Analysis of Public Sentiment Toward the Increase in VAT Rates Using the SVM Algorithm - Rahman, Elsa Azila; Lubis, Aidil Halim (Indonesian Journal of Data Science, IoT, Machine Learning and Informatics, 2025)
The Policy Of Increasing the Value Added Tax (VAT), particularly on luxury goods as stipulated in Minister of Finance Regulation (PMK) Number 131 of 2024, has sparked various public responses, many of which are captured through social media. In today's digital era, social media has become a primary platform for the public to express their opinions openly, including on government policies. This study aims to analyze public sentiment toward the VAT policy in order to provide insights for more responsive policymaking. A total of 4,000 comments were collected from the X platform using web crawling techniques, followed by preprocessing, resulting in 3,553 clean comments. Sentiment labeling was conducted automatically using a lexicon-based approach, which revealed that the majority of comments expressed positive sentiment (73.3%), while the remainder were negative (26.7%). Sentiment classification was performed using the Support Vector Machine (SVM) algorithm with a polynomial kernel and an 80:20 training-testing data split. Evaluation results showed that the model achieved an accuracy of 76.65%. The SVM model demonstrated excellent performance in detecting positive sentiment (precision 76.18%, recall 100%, and F1-score 86.51%), but was less effective in identifying negative sentiment (precision 100%, recall 7.78%, and F1-score 14.44%). These findings indicate that while the model is effective in recognizing positive opinions, further optimization is needed to improve performance in detecting negative sentiments.