Application of SVM and Naive Bayes with PSO for the Classification of Saloka Amusement Park Reviews

Indira Alifia  Putri; Khothibul  Umam; Maya Rini  Handayani; Hery  Mustofa

doi:10.37899/journallamultiapp.v6i6.2505

Indira Alifia Putri Information Technology, Faculty of Science and Technology, UIN Walisongo Semarang, Indonesia
Khothibul Umam Information Technology, Faculty of Science and Technology, UIN Walisongo Semarang, Indonesia
Maya Rini Handayani Information Technology, Faculty of Science and Technology, UIN Walisongo Semarang, Indonesia
Hery Mustofa Information Technology, Faculty of Science and Technology, UIN Walisongo Semarang, Indonesia

DOI: https://doi.org/10.37899/journallamultiapp.v6i6.2505

Keywords: Sentiment analysis, Naive Bayes, Support Vector Machine, Particle Swarm Optimization, Tourism reviews

Abstract

Visitor opinions on tourist destinations can be evaluated through sentiment analysis based on textual reviews. This study aimed to compare the performance of Support Vector Machine (SVM) and Naive Bayes (NB) algorithms in classifying visitor sentiments toward reviews of Saloka Theme Park, while also assessing the impact of parameter optimization using Particle Swarm Optimization (PSO). A total of 740 reviews were collected from the Traveloka platform and underwent text preprocessing. The optimization process targeted key parameters of each algorithm to improve the F1-score. Experimental results showed that the unoptimized SVM achieved an accuracy of 89 percent, while NB reached 86 percent. After applying PSO, SVM's accuracy dropped to 84 percent, whereas NB improved to 85 percent with more balanced classification across sentiment classes. These results recommend the integration of Naive Bayes with Particle Swarm Optimization as a potential approach for sentiment classification of tourism reviews, particularly in the case study of Saloka Theme Park.

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