Arminditya Fajri Akbar, Arminditya Fajri Akbar and Harry Budi Santoso, Harry Budi Santoso and Panca O. Hadi Putra, Panca O. Hadi Putra and Satrio Bhaskoro Yudhoatmojo, Satrio Bhaskoro Yudhoatmojo (2021) User Perception Analysis of Online Learning Platform “Zenius” During the Coronavirus Pandemic Using Text Mining Techniques. Jurnal Sistem Informasi (Journal of Information System), 17 (2). pp. 33-47. ISSN e-ISSN:2502-6631
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User Perception Analysis of Online Learning Platform “Zenius” During the Coronavirus Pandemic Using Text Mining Techniques.pdf - Other Download (567kB) |
Abstract
Availability of access to online learning platforms is expected to support online learning activities amid the COVID-19 pandemic. There are several issues experienced by students in online learning activities. These issues are related to Internet access, learning content, and learning evaluation. Online learning platforms are indispensable to be able to provide good learning resources that are easily accessible to students. This study aims to explore data reviews on Google Play Store to perceive the priority of service improvements that need to be carried out by online learning platform providers. Topic modeling and sentiment analysis are applied to extract useful information based on user sentiment towards the topics discussed in the application review. The result of topic analysis shows that topic trends in user reviews are about Live Class, Tryout, Subject Matter, User Account, Tutorial Video, and Free Learning Access. Meanwhile, the result of NRS assessment identified aspects that need to be a priority for improving online learning platform services. These aspects are in the Tryout and User Account sections. On the other hand, the aspect of Free Learning Access received the highest NRS. Users are helped by having free access to all learning content during learning from home activities.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | COVID-19, online learning platform, text mining, topic modeling, sentiment analysis |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Engineering Sciences |
| Depositing User: | Mrs Ni Made Yunia Dwi Savitri |
| Date Deposited: | 17 Nov 2022 01:30 |
| Last Modified: | 17 Nov 2022 01:30 |
| URI: | http://eprints.triatmamulya.ac.id/id/eprint/1729 |
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