Comparison of Support Vector Machine, Random Forest and XGBoost for Sentiment Analysis on Indodax

The rapid growth of digital assets like Bitcoin and cryptocurrencies has increased the need for secure trading platforms such as Indodax. With the growing number of users, reviews on platforms like Google Play Store provide valuable insights into user experience and satisfaction. This research applies Machine Learning methods to classify user review sentiments by comparing three main algorithms Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). One of the main challenge in sentiment analysis is the presence of irrelevant or redundant features, which can reduce model accuracy and increase computational costs. The Feature Selection Chi-Square technique is used to filter the most influential features, enhancing model efficiency without losing critical information. Experimental results show that SVM delivers the best performance compared to Random Forest and XGBoost. Before applying Chi-Square, SVM achieved 91% accuracy, which increased to 94% after applying the feature selection technique. The number of features used was reduced from 52,312 to 2,000 without significant information loss. This combination of SVM and Feature Selection Chi-Square proves to be an efficient and accurate solution for analyzing user sentiment on crypto trading platforms like Indodax. This method is expected to improve the responsiveness of trading applications to user needs and serve as a foundation for further research in Machine Learning-based sentiment analysis.