Szczegóły publikacji
Opis bibliograficzny
Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN / Herlina Jayadianti, Wilis Kaswidjanti, Agung Tri Utomo, Shoffan SAIFULLAH, Felix Andika DWIYANTO, Rafał DREŻEWSKI // Ilkom Jurnal Ilmiah ; ISSN 2087-1716. — 2022 — vol. 14 no. 3, s. 348-354. — Bibliogr. s. 352-354, Abstr. — Publikacja dostępna online od: 2022-12-20. — S. Saifullah - dod. afiliacja: Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia ; F. A. Dwiyanto - dod. afiliacja: Universitas Negeri Malang, Indonesia
Autorzy (6)
- Jayadianti Herlina
- Kaswidjanti Wilis
- Utomo Agung Tri
- AGHSaifullah Shoffan
- AGHDwiyanto Felix Andika
- AGHDreżewski Rafał
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 144918 |
|---|---|
| Data dodania do BaDAP | 2023-01-30 |
| Tekst źródłowy | URL |
| DOI | 10.33096/ilkom.v14i3.1505.348-354 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Ilkom Jurnal Ilmiah |
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.
