Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews

Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews

This study aims to evaluate and compare the performance of three machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naïve Bayes—for sentiment classification of user reviews on the NU Online application in the Google Play Store. NU Online is a religious digital...

Full description

Saved in:
Bibliographic Details
Main Authors: Heti Aprilianti, Khothibul Umam, Maya Rini Handayani
Format: Article
Language:English
Published: Politeknik Negeri Batam 2025-06-01
Series:Journal of Applied Informatics and Computing
Subjects:
sentiment analysis
support vector machine (svm)
nu online
user reviews
classification algorithms
Online Access:https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9482
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study aims to evaluate and compare the performance of three machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naïve Bayes—for sentiment classification of user reviews on the NU Online application in the Google Play Store. NU Online is a religious digital platform providing Islamic content such as articles, prayers, and worship schedules. A total of 1,500 user reviews were collected using web scraping, and 1,491 were retained after data cleaning. Preprocessing steps included punctuation removal, case folding, normalization, stopword removal, stemming, and tokenization. Sentiment labels (positive or negative) were automatically assigned using a lexicon-based approach. The performance of the models was assessed using accuracy, precision, recall, and F1-score, calculated via confusion matrix with a training-testing data split. The results show that the SVM with a linear kernel achieved the best accuracy (81.6%), followed by Naïve Bayes (73.2%) and K-NN (66.9%). These findings indicate that SVM is the most effective algorithm in this context, providing practical contributions for developers of the NU Online digital religious platform and contributing to research in Indonesian natural language processing.
ISSN:2548-6861

Similar Items