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...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-06-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9482 |
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| author | Heti Aprilianti Khothibul Umam Maya Rini Handayani |
| author_facet | Heti Aprilianti Khothibul Umam Maya Rini Handayani |
| author_sort | Heti Aprilianti |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8d056ff91978450eb7ade66534dfd01f |
| institution | Kabale University |
| issn | 2548-6861 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-8d056ff91978450eb7ade66534dfd01f2025-08-20T03:34:57ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-06-019392092710.30871/jaic.v9i3.94827027Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application ReviewsHeti Aprilianti0Khothibul Umam1Maya Rini Handayani2Teknologi Informasi, Fakultas Sains dan Teknologi, UIN Walisongo SemarangTeknologi Informasi, Fakultas Sains dan Teknologi, UIN Walisongo SemarangTeknologi Informasi, Fakultas Sains dan Teknologi, UIN Walisongo SemarangThis 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.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9482sentiment analysissupport vector machine (svm)nu onlineuser reviewsclassification algorithms |
| spellingShingle | Heti Aprilianti Khothibul Umam Maya Rini Handayani Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews Journal of Applied Informatics and Computing sentiment analysis support vector machine (svm) nu online user reviews classification algorithms |
| title | Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews |
| title_full | Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews |
| title_fullStr | Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews |
| title_full_unstemmed | Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews |
| title_short | Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews |
| title_sort | comparative study of svm knn and naive bayes for sentiment analysis of religious application reviews |
| topic | sentiment analysis support vector machine (svm) nu online user reviews classification algorithms |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9482 |
| work_keys_str_mv | AT hetiaprilianti comparativestudyofsvmknnandnaivebayesforsentimentanalysisofreligiousapplicationreviews AT khothibulumam comparativestudyofsvmknnandnaivebayesforsentimentanalysisofreligiousapplicationreviews AT mayarinihandayani comparativestudyofsvmknnandnaivebayesforsentimentanalysisofreligiousapplicationreviews |