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: | Indonesian |
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Universitas Muhammadiyah Purwokerto
2024-11-01
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| Series: | Jurnal Informatika |
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| Online Access: | https://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/21847 |
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| author | Slamet Riyadi Muhammad Dzaki Mubarok Cahya Damarjati Asnor Juraiza Ishak |
| author_facet | Slamet Riyadi Muhammad Dzaki Mubarok Cahya Damarjati Asnor Juraiza Ishak |
| author_sort | Slamet Riyadi |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-e457d1c9136d4d048f54f9a9e1a5d765 |
| institution | OA Journals |
| issn | 2086-9398 2579-8901 |
| language | Indonesian |
| publishDate | 2024-11-01 |
| publisher | Universitas Muhammadiyah Purwokerto |
| record_format | Article |
| series | Jurnal Informatika |
| spelling | doaj-art-e457d1c9136d4d048f54f9a9e1a5d7652025-08-20T02:13:06ZindUniversitas Muhammadiyah PurwokertoJurnal Informatika2086-93982579-89012024-11-0112215916710.30595/juita.v12i2.218476421Improving Sentiment Analysis Accuracy Using CRNN on Imbalanced Data: a Case Study of Indonesian National Football CoachSlamet Riyadi0Muhammad Dzaki Mubarok1Cahya Damarjati2Asnor Juraiza Ishak3Universitas Muhammadiyah YogyakartaUniversitas Muhammadiyah YogyakartaUniversitas Muhammadiyah YogyakartaUniversiti Putra MalaysiaConducting 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.https://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/21847sentiment analysis, deep learning, overfitting, oversampling, imbalanced data. |
| spellingShingle | Slamet Riyadi Muhammad Dzaki Mubarok Cahya Damarjati Asnor Juraiza Ishak Improving Sentiment Analysis Accuracy Using CRNN on Imbalanced Data: a Case Study of Indonesian National Football Coach Jurnal Informatika sentiment analysis, deep learning, overfitting, oversampling, imbalanced data. |
| 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 |
| topic | sentiment analysis, deep learning, overfitting, oversampling, imbalanced data. |
| url | https://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/21847 |
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