SENTIMENT ANALYSIS ON SIKSA KUBUR MOVIE REVIEWS: A COMPARATIVE STUDY BETWEEN NAÏVE BAYES AND SUPPORT VECTOR MACHINE METHOD - Linda Handayani; Andariza Permana (JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 2026)
This study examines user reviews of application X about the film Siksa Kubur by Joko Anwar which was released in 2024. The purpose of this study is to conduct user sentiment analysis and compare the results of sentiment analysis using the naïve bayes method and support vector machine based on training data scenarios and test data. The data collected through tweet harvest using the Python script on google colab is 3450 containing "siksa kubur". The research method used is an experimental method consisting of the stages of data collection, data preprocessing, feature extraction, model data training and evaluation. Sentiment is classified using the TextBlob library so that the most sentiment is neutral with 1254 tweets. Then the data is classified using the Naïve Bayes and SVM methods. Testing is carried out by dividing the training data scenario and training data, so that it is found that the best results are obtained with the training data scenario and test data 90%: 10%. The evaluation results obtained showed that the SVM method had the best results with accuracy, precision, recall and f1-score of 86%, 87%, 84% and 85% respectively based on the confusion matrix