• Indri Tri Julianto Jurusan Ilmu Komputer, Institut Teknologi Garut, Indonesia
  • Dede Kurniadi Jurusan Ilmu Komputer, Institut Teknologi Garut, Indonesia
  • Muhammad Rikza Nashrulloh Jurusan Ilmu Komputer, Institut Teknologi Garut, Indonesia
  • Asri Mulyani Jurusan Ilmu Komputer, Institut Teknologi Garut, Indonesia
Keywords: Analysis, Bitcoin, Sentiment, Twitter


Cryptocurrency trends, especially Bitcoin, have gained a place in a group of people and there are even countries that already use Bitcoin as a legal transaction tool. The dynamics that occur in this Bitcoin trend make many new users. This lack of understanding of the technology can cast doubt on those who want to get started, so it is necessary to conduct sentiment analysis to increase knowledge of what Bitcoin is and how it works. This study aims to conduct a Sentiment Analysis regarding Bitcoin through Twitter social media, so that their opinion on this technology will be known. The method used is by using Tweet data that has been downloaded on the website. The data is the result of using the Crawling technique, then sentiment analysis is carried out to classify a tweet into Neutral, Positive, or Negative. The results showed that from the 1998 dataset, 46.69% were classified as Neutral, then Positive, 43.54%, and 9.75% Negative.


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A. Firdaus and W. I. Firdaus, “Text Mining Dan Pola Algoritma Dalam Penyelesaian Masalah Informasi : (Sebuah Ulasan),” J. JUPITER, vol. 13, no. 1, pp. 66–78, 2021, [Online]. Available:

R. Parlika, S. I. Pradika, A. M. Hakim, and K. R. N. M, “Analisis Sentimen Twitter Terhadap Bitcoin dan Cryptocurrency Berbasis Python TextBlob,” J. Ilm. Teknol. Inf. dan Robot., vol. 2, no. 2, pp. 33–37, 2020.

A. P. Singh and S. Malani, “Understanding and Predicting Trends In Cryptocurrency Prices Using Data Mining Techniques,” IIIT Hyderabad, pp. 1–7, 2018.

R. C. Noorsanti, H. Yulianton, and K. Hadiono, “Blockchain - Teknologi Mata Uang Cryptocurrency,” Pros. SENDI_U 2018, pp. 306–311, 2018.

A. Afrizal, M. Marliyah, and F. Fuadi, “Analisis Terhadap Cryptocurrency (Perspektif Mata Uang, Hukum, Ekonomi Dan Syariah),” E-Mabis J. Ekon. Manaj. dan Bisnis, vol. 22, no. 2, pp. 13–41, 2021, doi: 10.29103/e-mabis.v22i2.689.

A. K. Fauziyyah and D. H. Gautama, “Analisis Sentimen Pandemi Covid19 Pada Streaming Twitter Dengan Text Mining Python,” J. Ilm. SINUS, vol. 18, no. 2, pp. 31–42, 2020, doi: 10.30646/sinus.v18i2.491.

F. F. Mailo and L. Lazuardi, “Analisis Sentimen Data Twitter Menggunakan Metode Text Mining Tentang Masalah Obesitas di Indonesia,” J. Inf. Syst. Public Heal., vol. 4, no. 1, pp. 28–36, 2019.

M. K. Hassan, F. A. Hudaefi, and R. E. Caraka, “Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data,” Stud. Econ. Financ., vol. 1, no. 1, pp. 1–22, 2021, doi: 10.1108/SEF-06-2021-0237.

D. Aprilia, D. Aji Baskoro, L. Ambarwati, and I. W. S. Wicaksana, “Belajar Data Mining Dengan Rapid Minner,” p. 139, 2013, [Online]. Available:

I. T. Julianto, D. Kurniadi, M. R. Nashrulloh, and A. Mulyani, “Comparison Of Data Mining Algorithm For Forecasting Bitcoin Crypto Currency Trends,” JUTIF, vol. 3, no. 2, pp. 245–248, 2022.

C. D. Manning, P. Raghavan, and H. Schütze, An Introduction to Information Retrieval (2nd edition). Cambridge: Cambridge University Press, 2009.

Han and Kamber, Data Mining Concepts and Technique. San Francisco: Diane Cerra, 2006.

I. H. Witten, E. Frank, and M. A. Hall, Data Mining Practical Machine Learning Tools and Technique. San Francisco: Morgan Kaufmann, 2011.

F. Rahutomo, A. Retno, T. Hayati, and P. N. Malang, “Evaluasi daftar stopword bahasa indonesia,” vol. 6, no. 1, 2019, doi: 10.25126/jtiik.201861226.

L. K. Harsono, Y. Alkhalifi, Nurajijah, and W. Gata, “Analisis Sentimen Stakeholder atas Layanan haiDJPb pada Media Sosial Twitter Dengan Menggunakan Metode Support Vector Machine dan Naïve Bayes,” J. Ilmu-ilmu Inform. dan Manaj., vol. 14, no. 1, pp. 36–44, 2020.

How to Cite
I. T. Julianto, D. Kurniadi, M. R. Nashrulloh, and A. Mulyani, “TWITTER SOCIAL MEDIA SENTIMENT ANALYSIS AGAINST BITCOIN CRYPTOCURRENCY TRENDS USING RAPIDMINER”, J. Tek. Inform. (JUTIF), vol. 3, no. 5, pp. 1183-1187, Oct. 2022.