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All Journal ILKOM Jurnal Ilmiah
Rafal Drezewski
AGH University of Science and Technology
Computer Science & IT

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Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN Herlina Jayadianti; Wilis Kaswidjanti; Agung Tri Utomo; Shoffan Saifullah; Felix Andika Dwiyanto; Rafal Drezewski
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1505.348-354

Abstract

Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.
Co-Authors Agung Tri Utomo Felix Andika Dwiyanto Jayadianti, Herlina Saifullah, Shoffan Wilis Kaswidjanti