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All Journal JURIKOM (Jurnal Riset Komputer) Bulletin of Computer Science Research
Iqbal Maulana
Universitas Singaperbangsa Karawang, Karawang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Sentimen Ulasan Pengguna Aplikasi Myim3 Pada Situs Google Play Menggunakan Support Vector Machine Piqih Aditiya; Ultach Enri; Iqbal Maulana
JURIKOM (Jurnal Riset Komputer) Vol 9, No 4 (2022): Agustus 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i4.4673

Abstract

Technological developments are increasingly rapid, this makes it easier to communicate information and shopping transactions, one of the innovations that are being adopted is digital services, such as self-service. One of the self-services is myim3 which is a product of PT Indosat Ooredoo Hutchison as an internet network service provider company, with the increasing number of users of the application, many opinions or public sentiments are shared in the comments or reviews column, therefore it is necessary to analyze this MyIM3 application review to find out public opinion about the application. The review data is obtained from the Google Play website which is retrieved using the scraping method with the help of 3rd party libraries in python. The amount of data obtained in this study was 3484 data. Experts assist in data labeling to determine positive and negative. In the preprocessing stage, the data is cleaned to reduce the less influential attributes. In the next stage, perform the transformation process with TF-IDF. The classification process is divided into several scenarios with the algorithm used as a support vector machine with 2 kernels, linear and RBF. The best results are in the scenario (70:30) for the linear kernel with 87% accuracy and the scenario (90:10) with 87% accuracy in the RBF kernel. The classification process produces the most frequently occurring words in each sentiment class which is visualized with a word cloud. The word "good" is the most dominant in the positive review data, while the word "network" is the most dominant in the harmful review data of the MyIM3 application
Perbandingan Naïve Bayes dan SVM untuk Analisis Sentimen Ulasan Kompas.id pada Data Tidak Seimbang Muhammad Ardana; Rini Mayasari; Iqbal Maulana
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.929

Abstract

The rapid advancement of digital technology and the increasing use of mobile devices have driven the widespread adoption of digital news applications, including Kompas.id. User reviews on the Google Play Store represent an important data source for understanding user satisfaction and emerging issues; however, the large volume of reviews makes manual analysis inefficient. Therefore, this study aims to compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying Kompas.id user reviews into positive, neutral, and negative sentiments. The research employs the Knowledge Discovery in Databases (KDD) framework, which includes web scraping, text preprocessing, lexicon-based sentiment labeling, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and classification and evaluation stages. The dataset consists of 1,023 cleaned reviews after data preprocessing. Model performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results indicate that Naïve Bayes achieves an accuracy of 72%, while SVM outperforms it with an accuracy of 80%, reflecting its stronger ability to handle high-dimensional and sparse textual feature spaces. Word cloud visualization reveals that positive sentiments are mainly associated with content quality, whereas negative sentiments are dominated by subscription-related issues and technical problems. Based on these findings, SVM is recommended as a more effective algorithm for sentiment analysis of digital news application reviews.
Co-Authors Muhammad Ardana Piqih Aditiya Rini Mayasari Ultach Enri