Sentiment Analysis of Public Opinion on Rupiah Redenomination Policy Using Support Vector Machine and SMOTE - Haidar Aslam; Haikal Nurul Barki; Adhi Prasetyo Wibowo; Faqih Al Araf; Abdul Hamid Musawir; Fuad Nur Hasan (Yayasan Kita Menulis, 2026)
The government’s planned rupiah redenomination has generated a substantial wave of public opinion across social media platforms. This study aims to analyze public sentiment by examining comments on YouTube and classifying them into two categories: positive and negative. The data are collected through web scraping conducted on December 21, 2025, using the keyword “rupiah redenomination.”Given the pronounced imbalance between negative and positive opinions, this study applies the Synthetic Minority Over-sampling Technique (SMOTE) to balance the class distribution within the training data. The research pipeline consists of text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and classification using a linear-kernel Support Vector Machine (SVM). Experimental results indicate that the SVM model achieves an accuracy of 88.28%. The application of SMOTE is shown to effectively enhance the model’s ability to identify the minority class, with the recall for positive sentiment reaching 0.71. Furthermore, the analysis reveals that public opinion is predominantly negative (83.93%), reflecting widespread concern regarding the potential economic implications of the policy.