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All Journal Jurnal Ilmu Komputer dan Informasi Prosiding KOMMIT TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Jurnal Sistem Informasi
Rina Refianti
Faculty of Computer Science and Information Technology, Gunadarma University
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : International Journal of Engineering, Science and Information Technology

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Sentiment Analysis Using Convolutional Neural Network Method to Classify Reviews on Zoom Cloud Meetings Application Based on Reviews on Google Playstore Refianti, Rina; Anggraeni, Novia
International Journal of Engineering, Science and Information Technology Vol 3, No 3 (2023)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v3i3.463

Abstract

Zoom Cloud Meetings is an application that is used to conduct video conferencing. On the Google Play Store, the Zoom Cloud Meeting application received a rating of 3.8, with 500 million more downloads as of March 2021. The application has many advantages, such as not being disturbed by pauses in conversation and having good video and audio quality. The advantages possessed by these applications require development so that application services are getting better. For this reason, user reviews are needed to see user satisfaction with the application so that they can determine services that can be developed in the future. Based on this, this research was created to create a web-based application that can classify user reviews of the Zoom Cloud Meetings application using the Convolutional Neural Network (CNN) method and calculate the accuracy value. This application is built using the Flask framework and the Python programming language. Model training is carried out using the TensorFlow library. Applications that have been made are then tested using two stages of testing, namely system testing with black box and data testing. Based on system testing, it was found that the website can run well, and for data testing using test data, the accuracy result is 91.5%.
Sentiment Analysis of Vidio Application Based on Reviews on Google Play Store Using Bidirectional Encoder Representations from the Transformers Method Refianti, Rina; Senjaya, Andrian
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1124

Abstract

Sentiment analysis is a computational study that aims to process, extract, summarise, and analyse the information contained in the text so it can conclude the emotions and points of view given by the author from the text and share the emotional tendencies in the text through the subjective information contained in it. Vidio is a video streaming site that allows users to watch and enjoy various videos and other services, such as live chat and playing games over the internet, and broadcast them by live streaming and video on demand. The analysis process uses the Bidirectional Encoder Representations from Transformers (BERT) method to classify comments into positive, neutral, and negative sentiments using the Python programming language, and based on the results of the tests that have been carried out from the amount of comment data—as much as 6000 data with training data as much as 4019 data, validation data as many as 1154 data, and test data as many as 569 data—an accuracy result of 76%.
Co-Authors Achmad Benny Mutiara Anastasia Monika Oktorianti Anggraeni, Novia Ariyanto, Ananda Satria HENDRA GUNAWAN B11211055 Jayawarsa, A.A. Ketut Nadia R.A. Mukarromah Putra, Ryan Arya Senjaya, Andrian