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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Ensiklopedia Social Review Al Jahiz: Journal of Biology Education Research
Febriani, Ayu
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Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Implementation of Support Vector Machine for Classifying User Reviews on the Sentuh Tanahku Application Febriani, Ayu; Khotibul Umam; Mokhammad Iklil Mustofa
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9832

Abstract

User reviews play a crucial role in the development of digital public service applications, as they reflect user satisfaction and service quality. This study aims to classify user reviews of the Sentuh Tanahku application into two sentiment categories, namely positive and negative, by applying the Support Vector Machine (SVM) algorithm. A total of 13,231 reviews obtained from Kaggle were processed through text preprocessing stages including case folding, tokenizing, stopword removal, and stemming. The TF-IDF technique was employed to convert text data into numerical vectors, followed by classification using SVM with hyperparameter tuning via RandomizedSearchCV. The evaluation results showed that the SVM model achieved an accuracy of 91% on training data and 84% on testing data. To assess its performance, the study compared SVM with baseline algorithms, namely Naïve Bayes and Logistic Regression. The comparison revealed that Logistic Regression and Naïve Bayes outperformed SVM with accuracy scores of 88.84% and 88.68%, respectively. Despite this, SVM remained competitive in maintaining balanced metrics across both classes. These findings highlight that algorithm performance in sentiment classification is highly influenced by the nature of the dataset. This study is expected to contribute as a reference for improving user opinion analysis methods in Indonesian-language public service applications.
Co-Authors Cut Muthiadin Fatmawati Nur Hafsan, Hafsan Khotibul Umam Khudaer, Faten Lili Salfina Mokhammad Iklil Mustofa Nichy Oktaviani