Comparison of Support Vector Machine and Random Forest Classification Methods in Dengue Fever Sentiment Analysis

Abstract

Dengue fever is a prevalent disease in Indonesia, caused by the dengue virus and transmitted by Aedes aegypti and Aedes albopictus mosquitoes. This study aims to analyze sentiment in tweets related to dengue fever using two machine learning algorithms: Support Vector Machine (SVM) and Random Forest. The dataset consists of 1,000 manually labeled tweets, categorized into three sentiment classes: Positive, Negative, and Neutral. The results indicate that the SVM algorithm outperformed Random Forest in terms of performance metrics. Specifically, SVM achieved higher accuracy (77.07%) compared to Random Forest (73.65%). Additionally, SVM demonstrated superior precision, recall, and F1-score, making it a more effective model for sentiment analysis in this context. These findings suggest that SVM is a promising tool for analyzing public sentiment on social media platforms, particularly for monitoring health-related issues like dengue fever. The study highlights the potential of machine learning techniques in improving public health surveillance and response by analyzing social media data for real-time insights.