Analisis Komentar Youtube Terhadap Kebijakan Bebas Impor Oleh Pemerintah Pusat Menggunakan Support Vector Machine - Ignasius Aditya Anggoro Putra; Salmon Salmon; Kusnandar Kusnandar (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
YouTube has become an important platform for expressing public opinion on government policies, including the free import policy. This study aims to analyze the sentiment of YouTube user comments regarding the free import policy using the Support Vector Machine (SVM) algorithm. The data were collected through web scraping using the YouTube Data API v3 from a Kompas.com video, resulting in 3,267 raw comments. The research stages include text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), lexicon-based sentiment labeling, and sentiment classification using SVM. To address data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the SVM model achieved an accuracy of 77.00% without tuning and 75.15% after hyperparameter optimization, with improved balance across sentiment classes. These findings indicate that SVM is effective for sentiment classification of YouTube comments.