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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Kemas M. Lhaksmana
Telkom University
Computer Science & IT Education

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Sentiment Analysis of Indonesian TikTok Review Using LSTM and IndoBERTweet Algorithm Jerry Cahyo Setiawan; Kemas M. Lhaksmana; Bunyamin Bunyamin
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 3 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i3.3911

Abstract

TikTok is currently the most popular app in the world and thus gets many reviews on the Google Play Store and other app marketplace platforms. These reviews are valuable user opinions that can be analyzed further for many purposes. Harnessing valuable analyses from these reviews can be obtained manually, which will be time-consuming and costly, or automatically with machine learning methods. This paper implements the latter with LSTM and IndoBERTweet, a derivative of BERT, using Indonesian vocabulary from Twitter post data. This research aims to determine the appropriate method to create a model that can automatically classify TikTok reviews into negative, neutral, and positive sentiments. The result demonstrates that IndoBERTweet outperforms the other, with an accuracy of 80%, whereas the LSTM accuracy is at 78%.
Co-Authors Bunyamin Jerry Cahyo Setiawan