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Dhuita, Windha Mega Pradnya
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Comparison of The Accuracy of K-Nearest Neighbor and Roberta Algorithm in Analysis of Sentiment on Miawaug Youtube Channel Comments Rahmawan, Fachrudin Okta; Hanafi; Dhuita, Windha Mega Pradnya
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1117

Abstract

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.
Co-Authors Hanafi Rahmawan, Fachrudin Okta