Comparative Study of SVM and Decision Tree Algorithms on the Effect of SMOTE Technique on LinkAja Application - Faruq, Muhammad Kholfan; Umam, Khothibul; Mustofa, Mokhamad Iklil; Mahfudh, Adzhal Arwani (Politeknik Negeri Batam, 2025)
The widespread adoption of digital wallets like LinkAja in Indonesia has led to a surge in user-generated reviews, which are valuable for assessing service quality. This study compares the classification performance of Support Vector Machine (SVM) and Decision Tree algorithms on user reviews from the LinkAja application. 7.000 reviews were gathered through web scraping and processed with standard text cleaning, tokenization, stopword removal, and stemming, resulting in 6,261 usable entries. These were divided into training and testing sets in a 70:30 ratio. The performance of each algorithm was evaluated both before and after the application of Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Prior to SMOTE, SVM recorded an accuracy of 77.97%, precision of 0.74, recall of 0.33, and F1 score of 0.45, while Decision Tree reached 72.01% accuracy, 0.50 precision, 0.62 recall, and 0.55 F1 score. After SMOTE, SVM accuracy slightly improved to 78.29%, with notable increases in recall (0.74) and F1 score (0.60); Decision Tree also saw an accuracy rise to 74.56% but experienced a slight decline in F1 score to 0.52. These findings demonstrate that SVM, particularly when used with SMOTE, offers better overall performance and class balance in classifying reviews with imbalanced sentiment distribution, making it more suitable than Decision Tree for this application.