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Ignasius Aditya Anggoro Putra
STMIK Widya Cipta Dharma, Samarinda
Computer Science & IT

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Analisis Komentar Youtube Terhadap Polemik Ijazah Presiden Ke 7 Indonesia Menggunakan Support Vector Machine Ignasius Aditya Anggoro Putra; Salmon Salmon; Kusnandar Kusnandar
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.883

Abstract

This study aims to analyze public sentiment toward the controversy surrounding President Joko Widodo’s academic credentials by examining user comments on YouTube. A total of 20,294 comments were collected and processed through text cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labels were assigned using a lexicon-based approach, producing positive, negative, and neutral categories. The experimental results indicate that the combination of SVM, TF-IDF, and SMOTE achieved strong classification performance, with an accuracy of 86.87%. The model demonstrated better performance in identifying negative and neutral sentiments, while some positive sentiments tended to be misclassified as neutral. Overall, this study shows that sentiment analysis based on YouTube comments can serve as an effective approach for mapping public opinion on socio-political issues in an automated and large-scale manner. Feature extraction utilized Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification was performed using a Support Vector Machine (SVM). The model achieved an accuracy of 86.87% and a macro F1-score of 0.87, indicating that the integration of TF-IDF, SMOTE, and SVM is effective for large-scale sentiment classification of YouTube comments related to socio-political issues.
Analisis Komentar Youtube Terhadap Kebijakan Bebas Impor Oleh Pemerintah Pusat Menggunakan Support Vector Machine Ignasius Aditya Anggoro Putra; Salmon Salmon; Kusnandar Kusnandar
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.995

Abstract

YouTube has become an important platform for expressing public opinion on government policies, including the free import policy. This study aims to analyze the sentiment of YouTube user comments regarding the free import policy using the Support Vector Machine (SVM) algorithm. The data were collected through web scraping using the YouTube Data API v3 from a Kompas.com video, resulting in 3,267 raw comments. The research stages include text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), lexicon-based sentiment labeling, and sentiment classification using SVM. To address data imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the SVM model achieved an accuracy of 77.00% without tuning and 75.15% after hyperparameter optimization, with improved balance across sentiment classes. These findings indicate that SVM is effective for sentiment classification of YouTube comments.
Co-Authors Kusnandar Kusnandar Salmon Salmon