Analisis Sentimen Bull dan Bear Market Bitcoin Pada Komentar YouTube Menggunakan Algoritma Support Vector Machine

Hardi Wirkan (Universitas Pelita Bangsa, Cikarang)
Andri Firmansyah (Universitas Pelita Bangsa, Cikarang)

TIN: TERAPAN INFORMATIKA NUSANTARA
Vol 7 No 1 (2026): June 2026

The development of the cryptocurrency market, particularly Bitcoin, significantly influences public opinion expressed through social media platforms such as YouTube. This study aims to analyze bull and bear market sentiments in YouTube comments about Bitcoin using the Support Vector Machine (SVM) algorithm. A total of 25,000 comments were collected using the YouTube Data API. After preprocessing stages including case folding, cleaning, tokenizing, word normalization, and stopword removal, 8,991 valid data were obtained. The dataset was divided into 7,192 training data (80%) and 1,799 testing data (20%). TF-IDF weighting was applied before classification using the SVM algorithm. The evaluation results show that the model achieved an accuracy of 98%, with a macro F1-score of 0.91 and a weighted F1-score of 0.98. Sentiment distribution in the testing data indicates 50.42% neutral (907 comments), 47.08% positive (847 comments), and 2.50% negative (45 comments). The dominance of neutral and positive sentiments reflects relatively stable and optimistic public opinion, consistent with the upward trend of the cryptocurrency market in 2024. This study contributes by providing a YouTube comment-based sentiment analysis approach to describe public opinion tendencies toward Bitcoin bull and bear market conditions, while also offering empirical evidence regarding the effectiveness of the Support Vector Machine algorithm in classifying sentiment within high-dimensional text-based social media data. Furthermore, the findings provide insights into the relationship between public opinion tendencies and cryptocurrency market conditions, which may serve as a reference for understanding market psychology through social media-based sentiment analysis. This study demonstrates that the SVM algorithm is effective in classifying YouTube comment sentiments related to Bitcoin market conditions.