Knowbase : International Journal of Knowledge in Database - Vol. 02 No. 02 (July-December 2022) pp, 142-155 Published online on the page : https://ejournal.iainbukittinggi.ac.id/index.php/ijokid/ Knowbase : International Journal of Knowledge in Database | ISSN (Print) 2798-0758 | ISSN (Online) 2797-7501 | http://dx.doi.org/10.30983/ijokid.v2i2.5906 Creative Commons Attribution-ShareAlike 4.0 International License. Some rights reserved Sentiment Analysis And Topic Modeling on User Reviews of Online Tutoring Applications Using Support Vector Machine and Latent Dirichlet Allocation Mohammad Rezza Fahlevvi 1,* 1 Institut Pemerintahan Dalam Negeri, Bandung, Indonesia Article Information ABSTRACT Article History: Accepted by the Editor: October 27, 2022 Final Revision: October 26, 2022 Published Online: November 28, 2022 Ruangguru is an online non-formal education application in Indonesia. There are several appealing features that encourage students to study online. The app's release on the Google Play Store will assist app developers in receiving feedback through the review feature.Users submit various topics and comments about Ruangguru in the review feature of Ruangguru, making it difficult to manually identify public sentiments and topics of conversation. Opinions submitted by users on the review feature are interesting to research further. This study aims to classify user opinions into positive and negative classes and model topics in both classes. Topic modeling aims to find out the topics that are often discussed in each class. The stages of this study include data collection, data cleaning, data transformation, and data classification with the Support Vector Machine method and the Latent Dirichlet Allocation method for topic modeling. The results of topic modeling with the LDA method in each positive and negative class can be seen from the coherence value. Namely, the higher the coherence value of a topic, the easier the topic is interpreted by humans. The testing process in this study used Confusion Matrix and ROUGE. The results of model performance testing using the Confusion Matrix are shown with accuracy, precision, recall, and f-measure values of 0.9, 0.9, 0.9, and 0.89, respectively. The results of model performance testing using ROUGE resulted in the highest recall, precision, and f-measure of 1, 0.84, and 0.91. The highest coherence value is found in the 20th topic, with a value of 0.318. Using the Support Vector Machine and Latent Dirichlet Allocation algorithms are considered adequate for sentiment analysis and topic modeling for the Ruangguru application. This is an open access article under the CC–BY-SA license Key Word Ruangguru Google Play Store (GPS) Sentiment Analyst Topic Modelling Support Vector Machine Latent Dirichlet Allocation (LDA) Confusion Matrix Rouge Correspondence E-mail: rezza@ipdn.ac.id* 1. Introduction The rapid development of technology, especially the Internet, opens opportunities to develop information services in educational institutions [1] —the Internet benefits every area of business, academia (education), government, and organizations. The Indonesian Internet Service Providers Association (APJII) grew internet use from 143.26 million to 171.17 million. Thus, of the total population of Indonesia, 64.8% has been connected to the Internet [2]. The electronic learning medium, as a controller, is operated by the user so that he can control and access needs. The study ‖Interactions in online learning: Important factors and research evidence from the literature‖ shows that most factors proved that traditional parameters apply to the environment. Online