Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen

Sharazita Dyah Anggita (Universitas Amikom Yogyakarta Indonesia)
Ferian Fauzi Abdulloh (Universitas AMIKOM Yogyakarta)

Journal of Applied Computer Science and Technology (JACOST)
Vol 4 No 1 (2023): Juni 2023

Sentiment analysis is a method for processing consumer reviews. This study examines the application of the Support Vector Machine (SVM) algorithm based on PSO and Information Gain as feature selection to filter attributes as a form of optimization. Algorithm implementation in sentiment analysis is carried out by applying a test scenario to measure the level of accuracy of the several parameters used. Selection of the Information Gain feature using the top-k parameter yields an accuracy value of 85.3%. Algortima optimization applying information gain feature selection on the PSO-based SVM resulted in an optimal accuracy rate of 86.81%. The resulting increase in accuracy is 18.84% compared to the application of classic SVM without PSO-based information gain feature selection. Applying information gain feature selection on the PSO-based SVM algorithm can increase the accuracy value in the online sentiment review analysis.