Komparasi Algoritma Support Vector Machine dan Decision Tree Dalam Analisis Sentimen Publik Terhadap Penerapan PPN 12%
-
Djalu Bintang Putra
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
- Ryan Randy Suryono Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia (*) Corresponding Author
Abstract
The implementation of the 12% Value-Added Tax (VAT) policy in Indonesia has generated various reactions from the public, both positive and negative. To understand public perception, researchers compared the performance of two algorithms, namely Support Vector Machine (SVM) and Decision Tree, in analyzing sentiment on social media. A total of 7,965 tweets were collected from the X (Twitter) platform using web scraping techniques and processed through several stages, including text cleaning, tokenization, stopword removal, stemming, and data balancing using the SMOTE method to improve model accuracy. The evaluation results showed that SVM achieved 80% accuracy, higher than Decision Tree, which only reached 68%. Based on these findings, it can be concluded that SVM is more effective in analyzing public sentiment regarding the 12% VAT policy. These findings can serve as a reference for the government and relevant stakeholders to better understand public opinion and design more suitable policies. This study also provides opportunities for further development by exploring other algorithms or more advanced data processing techniques to enhance the accuracy and effectiveness of sentiment analysis in the future.
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