Performance of SVM Optimized with PSO as Classification Method for Sentiment Analysis UNNES’s Social Media

Abstract

The rapid growth of Big Data, particularly from social media platforms, presents organizations with vast opportunities for extracting valuable insights. For educational institutions like UNNES, sentiment analysis can be crucial for monitoring and enhancing public perception. This research explores the application of sentiment analysis using SVM optimized by PSO to improve classification accuracy. Although SVM is widely known for its effectiveness in linearly separable data, it struggles with nonlinear data. By employing kernel functions and optimizing hyperparameters through PSO, this study aims to improve SVM’s performance. The results show that the optimized SVM model with the RBF kernel and PSO achieved an accuracy of 82.05%, compared to 80.96% using standard SVM, demonstrating a 1.09% improvement. These findings indicate that PSO significantly enhances the efficiency and accuracy of SVM models in sentiment analysis, making it a powerful tool for analyzing social media data in educational contexts.