PENERAPAN ALGORITMA NAÏVE BAYES DENGAN TEKNIK SMOTE UNTUK KLASIFIKASI SENTIMEN KURSUS ONLINE SKILL ACADEMY: APPLICATION OF THE NAÏVE BAYES ALGORITHM WITH SMOTE TECHNIQUE FOR SENTIMENT CLASSIFICATION OF SKILL ACADEMY ONLINE COURSES - Arsellina Milka Martin; Arif Nur Rohman (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
The rapid growth of online learning has led to the emergence of various e-learning platforms, including Skill Academy. However, not all courses are able to maintain learner engagement, partly due to discrepancies between user expectations and the quality of the provided materials. This study aims to classify user sentiment toward course titles by applying the Multinomial Naïve Bayes algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Data were collected through web scraping from five main public course pages, including information on course titles, prices, ratings, number of raters, release dates, and topic categories. Sentiment labels were assigned based on rating values, where ratings ≥ 4.0 were categorized as positive and ratings < 4.0 as negative. Text feature extraction was performed using the TF-IDF method. The experimental results show that the model developed without SMOTE achieved an accuracy of 89.36% but completely failed to identify the negative class, as indicated by a recall value of 0%. After applying SMOTE to the training data, the recall for the negative class increased significantly to 64% demonstrating a substantial improvement in the model’s ability to detect previously overlooked negative sentiment. Although a slight decrease in accuracy was observed in several testing scenarios, the improvement in recall and F1-score for the minority class represents the primary contribution of this study. These findings confirm that SMOTE is effective in mitigating class imbalance and enhances sentiment analysis performance for short text data on online course platforms.