Sentiment Analysis Of Reading Difficulties In Grade 7 Secondary School Students Using The Support Vector Machine (SVM) Algorithm - Algipari, Rasyid Zanuar; Tresnawati, Shandy (Politeknik Negeri Lhokseumawe, 2025)
The reading ability of junior high school students in Indonesia is still relatively low, as evidenced by the results of the National Assessment and the viral case of 29 grade VII junior high school students in Pangandaran who are not yet fluent in reading. This study aims to analyze public perception of this phenomenon through a sentiment analysis approach based on YouTube comment text and using the Support Vector Machine (SVM) algorithm. The steps of the SEMMA method are applied, starting with collecting comment data, preprocessing the text using dictionary-based methods and TF-IDF, and finally classification using SVM. The dataset used includes 1,055 comments. The results of the study show that the SVM algorithm is able to classify sentiment into three categories (positive, negative, and neutral) with an accuracy of 87%. These results indicate that the majority of people care about the quality of basic education. This study contributes to the computational understanding of public perception and can be used as a reference for data-based literacy guidelines.