Klasifikasi Opini Pengguna TikTok terhadap Keamanan dan Efektivitas Produk Skincare Lokal menggunakan Metode Naïve Bayes, Decision Tree, dan Random Fores - Sintia Ariyani; Styawati Styawati (Forum Kerjasama Pendidikan Tinggi, 2026)
This study aims to analyze and compare the performance of Naïve Bayes, Decision Tree, and Random Forest algorithms in classifying TikTok users’ opinions regarding the safety and effectiveness of local skincare products. The results show that these algorithms exhibit significant differences in performance for sentiment classification tasks. Before applying SMOTE, Random Forest achieved the highest accuracy of 87%, followed by Decision Tree at 79% and Naïve Bayes at 65%. The main weakness was observed in minority classes such as Safe and Unsafe, which had low recall values. After applying SMOTE, all models showed improved performance, particularly in recognizing minority classes, resulting in more balanced accuracy, precision, recall, and F1-score across all sentiment categories. The TF-IDF analysis revealed that the extracted features were still dominated by common words and brand names, indicating that they did not fully represent the specific aspects of safety and effectiveness. This suggests that the preprocessing and feature selection stages can be further improved to generate more relevant feature representations. The classification visualization showed that most comments were categorized as Effective and Ineffective, while the Neutral category contained fewer instances. The implementation of SMOTE improved model performance in handling imbalanced data; however, it must be applied carefully only to the training data to avoid evaluation bias. Overall, Random Forest demonstrated the best performance among the evaluated algorithms. This study contributes to the development of a multi-class sentiment analysis model capable of distinguishing between safety and effectiveness aspects of skincare products, and demonstrates that the application of SMOTE effectively improves classification performance on imbalanced datasets. Future research is recommended to enhance sentiment labeling methods, improve feature quality, and explore more advanced approaches such as deep learning to achieve more accurate and robust classification results.