SENTIMENT ANALYSIS OF THE ENGLISH LEARNING YOUTUBE CHANNEL FOR TOEFL STUDY RECOMMENDATIONS USING THE SVM METHOD - Gani Adi Alzani Rusandi; Dede Syahrul Anwar; Rudi Hartono (ADVANCE INFORMATICS RESEARCH JOURNAL, 2025)
The use of social media in Indonesia is not only for entertainment but also as a means of education. YouTube, as one of the most popular sites in the world, is used for learning including TOEFL exam preparation. Tasikmalaya University of Struggle students often have difficulty choosing appropriate learning resources among the many English learning channels such as Andrian Permadi, Yanto Tanjung, and Rumah Smart English. This research aims to overcome this problem by analyzing the sentiment of YouTube users' comments on these channels using the Support Vector Machine method. The research stages include collecting comment data, data preprocessing, data labeling, and training the Support Vector Machine model for sentiment analysis. The research results show that the Yanto Tanjung channel got the highest accuracy score of 84%, making it the best choice for TOEFL preparation. The Andrian Permadi channel achieved 80% accuracy, and the Rumah Smart English channel achieved 75% accuracy. The contribution of this research is to provide recommendations based on sentiment analysis to help students choose appropriate YouTube channels for learning English, thereby maximizing their preparation for the TOEFL exam.