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All Journal REKAYASA JOIN (Jurnal Online Informatika) SENTIA 2017 SENTIA 2016 Jurnal Informatika dan Rekayasa Elektronik Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) GUYUB: Journal of Community Engagement TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Nusantara Journal of Computers and its Applications Proceeding Of International Conference On Education, Society And Humanity
Fadli Hidayat, M. Noer
Universitas Nurul Jadid
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : Jurnal Tekinkom (Teknik Informasi dan Komputer)

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ANALISIS SENTIMEN ULASAN APLIKASI PEMBELAJARAN DUOLINGGO DI PLAY STORE MENGGUNAKAN DISTILBERT Syindy Mauliddiyah; M.Noer Fadli Hidayat; Fathur Rizal
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1395

Abstract

Learning media innovation is currently required to keep up with the development of science and technology. Duolingo is a free language learning application. Duolinggo has been downloaded more than 500 million times and recorded more than 21 reviews in the comments column consisting of positive and negative comments. Duolinggo user reviews are classified into two sentiments, namely positive sentiment and negative sentiment. Sentiment analysis is an activity used to analyze a person's opinion or opinion on a topic, to support the classification, the algorithm used is DistilBERT. DistilBERT is a technique of how to make the BERT model smaller, but has similar qualities to a large model, distilBERT can be termed as 2 running models, namely the teacher model and the student model, the teacher model is a large model and is trained with a complete range of features such as the base (pre-trained model) The results of the DistilBERT algorithm for classifying 1000 reviews of the Duolingo learning application produce precision, recall, f1-score values on class 1 labels are 74%, 96%, and 84%, indicating that this BERT algorithm is very good at predicting label classes. With the accuracy result obtained is 80% in 85 seconds.
Co-Authors Abdillah Mundir Abu Tholib Achmad Febrianto Ahmad Fawaid Akil, Ahmad Ibnu Anando Setyo Adiwirawan Didik Dwi Prasetya Dwiki Qutsi Ali Multazam Erna Daniati Fathorazi Nur Fajri Fathur Rizal Fauzan Abdul Latif Indah Sri Mulyati Jose, Anthonio Fernando Kamil Malik Lailatul Qomariyah, Lailatul Misbahul Munir Moh. Furqan Mohammad Alfian Faisol Mustaqim Muhammad Najibulloh Muzaki Nabila, Uyun Nisa', Chairun Qurrotu Aini, Qurrotu Rahman, Handono Fatkhur Resty Wulanningrum Siti Helmyati Soleh, Paisal Suci Rahayu Agustini Sucipto Sucipto Supri yono Syaad Patmanthara Syindy Mauliddiyah Triyanna Widiyaningtyas Wahab Sya’roni Yanuar, Rizqi Alif