Analisis Sentimen Bull dan Bear Market Bitcoin Pada Komentar YouTube Menggunakan Algoritma Support Vector Machine
-
Hardi Wirkan
*
Universitas Pelita Bangsa, Cikarang,
Indonesia
- Andri Firmansyah Universitas Pelita Bangsa, Cikarang, Indonesia (*) Corresponding Author
Abstract
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.
Downloads
References
Albab, M. U., P., Y. K., & Fawaiq, M. N. (2023). Optimization of the Stemming Technique on Text Preprocessing President 3 Periods Topic. Jurnal Transformatika, 20(2), 1–12. https://doi.org/10.26623/transformatika.v20i2.5374
Arimbi Puspitasari, Diana Sava Salsabila, & Dwi Roliawati. (2025). Penerapan ResNet50-CNN untuk Optimalisasi Klasifikasi pada Data Fashion. Indonesian Journal on Data Science, 3(1), 1–12. https://doi.org/10.30989/ijds.v3i1.1533
Bitcoin, C. (2024). Return Dan Risiko Investasi Terhadap Volume Perdagangan Cryptocurrency Bitcoin. Jurnal Bisnis & Akuntansi Unsurya, 9(1), 1–11. https://doi.org/10.35968/jbau.v9i1.1168
Hidayat, S., Herlina, N., & Nurul, G. (2024). Penerapan Model Support Vector Machine Pada Kasus Klasifikasi Teks Berdasarkan Tujuan SDGS Ke Tiga, Empat, Dan Enam. SisInfo, 6(2), 28–37. https://doi.org/10.37278/sisinfo.v6i2.893
I Putu Gede Hendra Suputra, Linawati, Sukadarmika, I. G., & Sastra, N. P. (2025). Klasifikasi Judul Berita Bahasa Indonesia Menggunakan Support Vector Machine Dan Seleksi Fitur Mutual Information. Jurnal Pendidikan Teknologi Dan Kejuruan, 22(1), 69–79. https://doi.org/10.23887/jptkundiksha.v22i1.89158
Nur, M. (2026). Apakah Bitcoin Masih Layak sebagai Instrumen Investasi di Tengah Volatilitas Ekstrem? Studi terhadap Perilaku Investasi Generasi Z. Salewangang: Jurnal Ekonomi, 20(1), 43–53.
Pinaria, A. P. P., Widodo, & Nugraheni, M. (2024). Analisis Sentimen Rancangan Undang-Undang Tindak Pidana Kekerasan Seksual/Undang-Undang Tindak Pidana Kekerasan Seksual Pengguna Twitter Menggunakan Algoritma Naïve Bayes Classifier. PINTER : Jurnal Pendidikan Teknik Informatika Dan Komputer, 8(2), 1–10. https://doi.org/10.21009/pinter.8.2.1
Putra, I. G. S. D., & Putra, I. N. T. A. (2025). Implementasi Metode Naïve Bayes Pada Analisis Sentimen Pengguna Aplikasi Mobile Kita Bisa. Jurnal Informatika Dan Teknik Elektro Terapan, 13(2). https://doi.org/10.23960/jitet.v13i2.6423
Putri, S. R., Asrianda, A., & Rosnita, L. (2025). Sentiment Analysis of Youtube and Gotube Reviews on Google Play Using the Support Vector Machine (SVM) Method in Indonesia. Journal of Applied Informatics and Computing, 9(3), 1025–1033. https://doi.org/10.30871/jaic.v9i3.9461
Rifka Alkhilyatul Ma’rifat, I Made Suraharta, I. I. J. (2024). Analisis Sentimen Untuk Mengukur Ulasan Pengguna Aplikasi Mobile Legend Menggunakan Algoritma Naive Bayes, SVM, Random Fores, Decision Tree, dan Logistic Regression. Jurnal Sistem Informasi, 2(1), 306–312. https://doi.org/10.18495/jsi.v16i1.152
Saputra, A. N. A., Saputro, R. E., & Saputra, D. I. S. (2025). Enhancing Sentiment Analysis Accuracy Using SVM and Slang Word Normalization on YouTube Comments. Sinkron, 9(2), 687–699. https://doi.org/10.33395/sinkron.v9i2.14613
Sari Siregar, Y., Handoko, D., Khairani, M., Syahputri, N. I., & Harahap, H. (2024). Implementasi Data Mining Klasifikasi Algoritma Chaid Dalam Menentukan Pola Penerima Mahasiswa Baru. Digital Transformation Technology, 3(2), 978–989. https://doi.org/10.47709/digitech.v3i2.3612
Suharsono, J. P., & Nurahman, D. (2024). Pemanfaatan Youtube Sebagai Media Peningkatan Pelayanan Dan Informasi. Ganaya : Jurnal Ilmu Sosial Dan Humaniora, 7(1), 298–304. https://doi.org/10.37329/ganaya.v7i1.3157
Suryadewiansyah, M. K., & Tju, T. E. E. (2022). Naïve Bayes dan Confusion Matrix untuk Efisiensi Analisa Intrusion Detection System Alert. Jurnal Nasional Teknologi Dan Sistem Informasi, 8(2), 81–88. https://doi.org/10.25077/teknosi.v8i2.2022.81-88
Syahrohim, I., Saputra, S. D., Saputra, R. W., Pranatawijaya, V. H., & Priskila, R. (2024). Perbandingan Analisis Sentimen Setelah Pilpres 2024 Di Twitter Menggunakan Algoritma Machine Learning. JITET (Jurnal Informatika Dan Teknik Elektro Terapan), 12(2). https://doi.org/10.23960/jitet.v12i2.4249
Tjut Adek, R., Fitri, Z., & Siregar, S. C. (2025). Analisis Sentimen Komentar Pada Saluran Youtube Beauty Vlogger Berbahasa Indonesia Menggunakan Metode Support Vector Machine. Jurnal Algoritme, 5(2), 164–175. https://doi.org/10.35957/algoritme.v5i2.9692
Tri Ayu Mareta, Desty Endrawati Subroto, Lailaturrohmah Aulia, Siti Nuryanah, & Ratu Najwa Fadilah. (2025). Peran Media Sosial Youtube sebagai Media Edukasi dalam Pendidikan Generasi Z. Guruku: Jurnal Pendidikan Dan Sosial Humaniora, 3(1), 98–106. https://doi.org/10.59061/guruku.v3i1.894
William, W., Maryati, M., Khesi, K., Alvina, J., & Selina. Ng, S. N. (2022). Analisis Kebijakan Pemerintah Terkait Ancaman Pengangguran Pasca Inflasi di Negara Indonesia. Jurnal Multidisiplin Indonesia, 1(4), 1066–1073. https://doi.org/10.58344/jmi.v1i4.101
Yanto, R., Kesuma, H. Di, Afrudi, S., Komputer, I., Indo, U., Mandiri, G., Rawas, U. M., Rawas, M., & Info, A. (2025). Implementasi Text Mining Dalam Mengidentifikasi Similarity Judul Penelitian. Djtechno : Jurnal Teknologi Informasi, 6(1), 237–251. https://doi.org/10.46576/djtechno
Zahra, F. A., Setiaji, P., & Triyanto, W. A. (2025). Klasifikasi Ekspresi Emosi Wajah Bahagia dan Tidak Bahagia Menggunakan Arsitektur Mobilenetv2 Berbasis Deep Learning. Jurnal Sistem Informasi Dan Teknologi (SINTEK), 1(2), 1–6. https://doi.org/10.24176/sitech.v8i1.15546
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Bull dan Bear Market Bitcoin Pada Komentar YouTube Menggunakan Algoritma Support Vector Machine
Copyright (c) 2026 Hardi Wirkan, Andri Firmansyah
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
