Analisis Sentimen Komentar YouTube Kualifikasi Piala Dunia 2026 Ronde 4 Timnas Indonesia menggunakan Naïve Bayes
-
Rasyid Iskandar Prayogi
*
Universitas Singaperbangsa Karawang, Telukjambe Timur,
Indonesia
- Azhari Ali Ridha Universitas Singaperbangsa Karawang, Telukjambe Timur, Indonesia
- Ade Andri Hendriadi Universitas Singaperbangsa Karawang, Telukjambe Timur, Indonesia
- Nina Sulistyowati Universitas Singaperbangsa Karawang, Telukjambe Timur, Indonesia
- Taufik Ridwan Universitas Singaperbangsa Karawang, Telukjambe Timur, Indonesia (*) Corresponding Author
Abstract
Public interest in the Indonesian National Team’s 2026 World Cup Qualifiers is evident from the diverse responses expressed in YouTube comments. The diversity of these responses indicates a range of sentiments toward the Indonesian National Team’s performance, making it necessary to cluster the comments to identify emerging sentiment trends. This study aims to analyze the sentiment of YouTube comments regarding the Indonesian National Team during Round 4 of the 2026 World Cup Qualifiers using the Multinomial Naïve Bayes algorithm. A total of 17,404 comments were collected via the YouTube Data API v3 from the Indonesian National Team and RCTI Sports channels. The data then underwent preprocessing, labeling using the InSet Lexicon verified by linguists, weighting using Term Frequency-Inverse Document Frequency (TF-IDF), and classification with the Multinomial Naïve Bayes algorithm. Testing was conducted using three data split scenarios 80:20, 75:25, and 70:30 with the metrics of accuracy, precision, recall, and F1-score. The test results showed that the 70:30 data split yielded the highest accuracy of 79.09%. In that scenario, the negative class achieved a precision of 0.81, a recall of 0.93, and an F1-score of 0.87. The negative class was also the most dominant sentiment, while the number of data points in the neutral and positive classes was lower. These results indicate that Multinomial Naïve Bayes can be used to classify YouTube comments.
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