Comparison of naïve bayes and support vector machine methods for jkt48 music video comment classification - Abdul Aziz, Alif; Rofik, Rofik (Journal of Student Research Exploration, 2025)
The research was conducted to discuss the classification of comments on music video JKT48 "Magic Hour" in YouTube using method Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). YouTube monitors viewer emotion by adjective comments Adjectives are the descriptive powers of human communication we use to help personify how different types, i.e. different "personalities" flavors and depths reflect artistic expressions The place where interactivity meets with digital marketing signifying a shared contribution to music lore In this work, we study the comparison of The Support Vector Machine (SVM) and Naive Bayes Classifier in terms of Accuracy, Precision & Recall. This Project includes data pre-processing, collecting the data by YouTube API and build classification models which involves Support Vector Machine and Naive Bayes Classifier. SVM displayed more stable performance than NBC, showing consistent results across different data split ratios. SVM achieved its highest accuracy of 93.42% at an 80:20 ratio, with precision and recall rates reaching 92.57% and 93.42%, respectively.