Penerapan Naive Bayes untuk Klasifikasi Opini Fans Manchester United pada Media Sosial - Muhammad Luthfi Lubis; Aidil Halim Lubis (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
This study examines the application of the Naïve Bayes algorithm to classify the opinions of Manchester United fans in Indonesian-language comments on YouTube. The diverse linguistic forms found in the comments such as slang, abbreviations, jokes, and sarcasm make manual analysis inefficient and potentially subjective. Data was collected from eight YouTube videos using the YouTube Data API v3 and stored in a MySQL database. Of the 4,886 comments obtained, 1,962 were identified as being in Indonesian. A total of 1,000 comments were used as ground truth, consisting of 400 positive, 300 negative, and 300 neutral comments. The data was divided into 80% training data and 20% test data using a stratified split. The processing stages included text preprocessing, TF-IDF weighting, Naïve Bayes classification, and evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The test results yielded an accuracy of 92.50%, a macro precision of 92.81%, a macro recall of 92.36%, and a macro F1-score of 92.57%. Of the 1,944 comments successfully classified, positive sentiment dominated at 47.58%, followed by negative at 28.34% and neutral at 24.07%. The web-based system, built using Laravel, PHP, and MySQL, is capable of integrating the processes of data extraction, labeling, classification, evaluation, and result visualization.