Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach
Conducting sentiment research on the perception of the Indonesian people towards Shin Tae Yong's (STY) role as coach of the Indonesian National Football Team (PSSI) is crucial as it can assist PSSI in determining whether to extend STY's contract. Prior studies have demonstrated that Deep L...
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| Format: | Article |
| Language: | English |
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Lembaga Publikasi Ilmiah dan Penerbitan Universitas Muhammadiyah Purwokerto
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/117530/ http://psasir.upm.edu.my/id/eprint/117530/1/117530.pdf |
| _version_ | 1848867273583886336 |
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| author | Riyadi, Slamet Mubarok, Muhammad Dzaki Damarjati, Cahya Ishak, Asnor Juraiza |
| author_facet | Riyadi, Slamet Mubarok, Muhammad Dzaki Damarjati, Cahya Ishak, Asnor Juraiza |
| author_sort | Riyadi, Slamet |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Conducting sentiment research on the perception of the Indonesian people towards Shin Tae Yong's (STY) role as coach of the Indonesian National Football Team (PSSI) is crucial as it can assist PSSI in determining whether to extend STY's contract. Prior studies have demonstrated that Deep Learning achieves a high level of accuracy when applied to sentiment analysis in many domains. Nevertheless, no investigation has been conducted thus far utilizing deep learning techniques to examine emotion towards STY. This study employs modified Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Convolutional Recurrent Neural Networks (CRNN), and CRNN models with and without data oversampling. The research findings indicate that the CRNN model, when combined with data oversampling and a redesigned architecture, achieves the highest level of accuracy (1.00) and consistently performs well. This research provides significant contributions in three areas: firstly, it utilizes Deep Learning techniques for sentiment analysis on STY; secondly, it modifies the CRNN architecture; and thirdly, it applies data oversampling to address the issue of imbalanced data. |
| first_indexed | 2025-11-15T14:33:53Z |
| format | Article |
| id | upm-117530 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:33:53Z |
| publishDate | 2024 |
| publisher | Lembaga Publikasi Ilmiah dan Penerbitan Universitas Muhammadiyah Purwokerto |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1175302025-05-29T02:27:57Z http://psasir.upm.edu.my/id/eprint/117530/ Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach Riyadi, Slamet Mubarok, Muhammad Dzaki Damarjati, Cahya Ishak, Asnor Juraiza Conducting sentiment research on the perception of the Indonesian people towards Shin Tae Yong's (STY) role as coach of the Indonesian National Football Team (PSSI) is crucial as it can assist PSSI in determining whether to extend STY's contract. Prior studies have demonstrated that Deep Learning achieves a high level of accuracy when applied to sentiment analysis in many domains. Nevertheless, no investigation has been conducted thus far utilizing deep learning techniques to examine emotion towards STY. This study employs modified Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Convolutional Recurrent Neural Networks (CRNN), and CRNN models with and without data oversampling. The research findings indicate that the CRNN model, when combined with data oversampling and a redesigned architecture, achieves the highest level of accuracy (1.00) and consistently performs well. This research provides significant contributions in three areas: firstly, it utilizes Deep Learning techniques for sentiment analysis on STY; secondly, it modifies the CRNN architecture; and thirdly, it applies data oversampling to address the issue of imbalanced data. Lembaga Publikasi Ilmiah dan Penerbitan Universitas Muhammadiyah Purwokerto 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/117530/1/117530.pdf Riyadi, Slamet and Mubarok, Muhammad Dzaki and Damarjati, Cahya and Ishak, Asnor Juraiza (2024) Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach. JUITA: Jurnal Informatika, 12 (2). pp. 159-167. ISSN 2579-8901; eISSN: 2086-9398 https://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/21847 10.30595/juita.v12i2.21847 |
| spellingShingle | Riyadi, Slamet Mubarok, Muhammad Dzaki Damarjati, Cahya Ishak, Asnor Juraiza Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach |
| title | Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach |
| title_full | Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach |
| title_fullStr | Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach |
| title_full_unstemmed | Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach |
| title_short | Improving sentiment analysis accuracy using CRNN on imbalanced data: a case study of Indonesian National Football coach |
| title_sort | improving sentiment analysis accuracy using crnn on imbalanced data: a case study of indonesian national football coach |
| url | http://psasir.upm.edu.my/id/eprint/117530/ http://psasir.upm.edu.my/id/eprint/117530/ http://psasir.upm.edu.my/id/eprint/117530/ http://psasir.upm.edu.my/id/eprint/117530/1/117530.pdf |