Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel Comments
This study aims to evaluate the accuracy of two algorithms, K-Nearest Neighbor (KNN) and Robustly Optimized BERT Approach (RoBERTa), in analyzing sentiment within comments on MiawAug’s YouTube channel. Sentiment analysis was conducted on two sentiment categories: binary classification (positive and...
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Center for Research and Community Service, Institut Informatika Indonesia Surabaya
2025-03-01
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| Series: | Teknika |
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| Online Access: | https://ejournal.ikado.ac.id/index.php/teknika/article/view/1117 |
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| author | Fachrudin Okta Rahmawan Hanafi Windha Mega Pradnya Dhuita |
| author_facet | Fachrudin Okta Rahmawan Hanafi Windha Mega Pradnya Dhuita |
| author_sort | Fachrudin Okta Rahmawan |
| collection | DOAJ |
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This study aims to evaluate the accuracy of two algorithms, K-Nearest Neighbor (KNN) and Robustly Optimized BERT Approach (RoBERTa), in analyzing sentiment within comments on MiawAug’s YouTube channel. Sentiment analysis was conducted on two sentiment categories: binary classification (positive and negative) and multi-class classification (positive, neutral, and negative). Using KNN, the binary classification yielded an accuracy of 86.12%, F1-score of 87.44%, recall of 96.64%, and precision of 79.89%. In contrast, the multi-class classification achieved 98.21% accuracy, F1-score, and recall with a precision of 98.23%. However, the RoBERTa model outperformed KNN, achieving 93.89% accuracy, 93.88% F1-score, 94.59% recall, and 93.22% precision in binary classification. For multi-class classification, RoBERTa further excelled, attaining 99.21% across accuracy, F1-score, recall, and precision. These findings demonstrate that RoBERTa surpasses KNN in sentiment analysis, especially in multi-class contexts, indicating its greater robustness for this application.
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| format | Article |
| id | doaj-art-fb7e1e96256445309d1b7b8ce51a105c |
| institution | DOAJ |
| issn | 2549-8037 2549-8045 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Center for Research and Community Service, Institut Informatika Indonesia Surabaya |
| record_format | Article |
| series | Teknika |
| spelling | doaj-art-fb7e1e96256445309d1b7b8ce51a105c2025-08-20T03:00:51ZengCenter for Research and Community Service, Institut Informatika Indonesia SurabayaTeknika2549-80372549-80452025-03-0114110.34148/teknika.v14i1.1117Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel CommentsFachrudin Okta Rahmawan0Hanafi1Windha Mega Pradnya Dhuita2Informatics Study Program, Universitas Amikom Yogyakarta, Sleman, DI Yogyakarta, IndonesiaMaster in Informatics Engineering, Universitas Amikom Yogyakarta, Sleman, DI Yogyakarta, IndonesiaMaster in Informatics Engineering, Universitas Amikom Yogyakarta, Sleman, DI Yogyakarta, Indonesia This study aims to evaluate the accuracy of two algorithms, K-Nearest Neighbor (KNN) and Robustly Optimized BERT Approach (RoBERTa), in analyzing sentiment within comments on MiawAug’s YouTube channel. Sentiment analysis was conducted on two sentiment categories: binary classification (positive and negative) and multi-class classification (positive, neutral, and negative). Using KNN, the binary classification yielded an accuracy of 86.12%, F1-score of 87.44%, recall of 96.64%, and precision of 79.89%. In contrast, the multi-class classification achieved 98.21% accuracy, F1-score, and recall with a precision of 98.23%. However, the RoBERTa model outperformed KNN, achieving 93.89% accuracy, 93.88% F1-score, 94.59% recall, and 93.22% precision in binary classification. For multi-class classification, RoBERTa further excelled, attaining 99.21% across accuracy, F1-score, recall, and precision. These findings demonstrate that RoBERTa surpasses KNN in sentiment analysis, especially in multi-class contexts, indicating its greater robustness for this application. https://ejournal.ikado.ac.id/index.php/teknika/article/view/1117Sentiment AnalysisK-Nearest Neighbor (KNN)RoBERTaYouTube Comments |
| spellingShingle | Fachrudin Okta Rahmawan Hanafi Windha Mega Pradnya Dhuita Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel Comments Teknika Sentiment Analysis K-Nearest Neighbor (KNN) RoBERTa YouTube Comments |
| title | Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel Comments |
| title_full | Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel Comments |
| title_fullStr | Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel Comments |
| title_full_unstemmed | Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel Comments |
| title_short | Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel Comments |
| title_sort | comparison of the accuracy of k nearest neighbor and roberta algorithm in analysis of sentiment on miawaug youtube channel comments |
| topic | Sentiment Analysis K-Nearest Neighbor (KNN) RoBERTa YouTube Comments |
| url | https://ejournal.ikado.ac.id/index.php/teknika/article/view/1117 |
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