Main Article Content
Bitcoin is a decentralized electronic money that is not controlled nor insured by a central authority. Because it is still a young system, the price of Bitcoin is extremely unpredictable, making Bitcoin users and investors uneasy. A typical difficulty for investors and traders is predicting the future movement of the value of Bitcoin electronic money based on historical data. Because investors and traders only notice swings in global currency prices and make Bitcoin buy/sell decisions instinctively, they frequently make the erroneous buy/sell decisions. Many investors and traders suffered significant losses as a result of this error. Losses can be reduced by employing an algorithm that predicts the movement of the value of Bitcoin electronic money. Using a comparison of two methodologies, the Simple Moving Average and RBFNN, we will anticipate the closing price of Bitcoin USD (BTC-USD) from January 1, 2021 to January 31, 2021. The results obtained using the simple moving average method MSE = 0.01 percent and MAPE = 36.67 percent, and the results obtained using the RBFNN method MSE = 9.97 x 10-7 and MAPE = 9.97 x 10-5, indicating that the RBFNN method with an accuracy rate of 99.9995 percent is better than the simple moving average method in forecasting the closing price of bitcoin.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with FIDELITY : Jurnal Teknik Elektro agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) 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.
Jurnal FIDELITY : Jurnal Teknik Elektro is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
J. Melitz, "Monetary Discipline, Germany, and the European Monetary System," IMF Work. Pap., vol. 6, no. 87, 1987, doi: http://dx.doi.org/10.2139/ssrn.884539.
A. Bulir, "Income inequalities: Does Inflation Matter?," IMF Work. Pap., pp. 21–34, 1998, doi: http://www.jstor.org/stable/4621662.
S. Tanwar, N. P. Patel, S. N. Patel, J. R. Patel, G. Sharma, and I. E. Davidson, "Deep Learning-Based Cryptocurrency Price Prediction Scheme with Inter-Dependent Relations," IEEE Access, vol. 48, no. 1, pp. 139–159, 2001, doi: 10.1109/ACCESS.2021.3117848.
S. Basco, "Globalization and financial development: A model of the Dot-Com and the Housing Bubbles," J. Int. Econ., vol. 92, no. 1, pp. 78–94, 2014, doi: 10.1016/j.jinteco.2013.10.008.
J. Bhosale and S. Mavale, "Volatility of select Crypto-currencies: A comparison of Bitcoin, Ethereum and Litecoin," Annu. Res. J. SCMS, Pune, vol. 6, no. March, pp. 132–141, 2018.
L. V. Trung Duong, D. Van Hieu, P. H. Luan, T. T. Hong, and L. Duc Khai, "Hardware Implementation for Fast Block Generator of Litecoin Blockchain System," Proc. - 2021 Int. Symp. Electr. Electron. Eng. ISEE 2021, vol. 2, pp. 139–144, 2021, doi: 10.1109/ISEE51682.2021.9418691.
S. Sihombing, M. Rizky Nasution, and I. Sadalia, "Analisis Fundamental Cryptocurrency terhadap Fluktuasi Harga: Studi Kasus Tahun 2019-2020," J. Akuntansi, Keuangan, dan Manaj., vol. 2, no. 3, pp. 213–224, 2021, doi: 10.35912/jakman.v2i3.373.
F. Valencia, A. Gómez-Espinosa, and B. Valdés-Aguirre, "Price movement prediction of cryptocurrencies using sentiment analysis and machine learning," Entropy, vol. 21, no. 6, 2019, doi: 10.3390/e21060589.
M. J. Hamayel and A. Y. Owda, "A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms," Ai, vol. 2, no. 4, pp. 477–496, 2021, doi: 10.3390/ai2040030.
N. Maleki, A. Nikoubin, M. Rabbani, and Y. Zeinali, Bitcoin Price Prediction Based on Other Cryptocurrencies Using Machine Learning and Time Series Analysis, vol. 0, no. 0. 2020.
M. M. Patel, S. Tanwar, R. Gupta, and N. Kumar, "A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions," J. Inf. Secur. Appl., vol. 55, no. May, p. 102583, 2020, doi: 10.1016/j.jisa.2020.102583.
V. Derbentsev, N. Datsenko, O. Stepanenko, and V. Bezkorovainyi, "Forecasting Cryptocurrency Prices Time Series Using Machine Learning Approach," in SHS Web of Conference 65, 02001, 2019, pp. 1–7, doi: https://doi.org/10.1051/shsconf/20196502001.
E. Irawan, F. Sembiring, S. Saepudin, and A. Erfina, "Implementasi Moving Average Terhadap Efektivitas Saldo Akun Trading Bitcoin," JURSISTEKNI (Jurnal Sist. Inf. dan Teknol. Informasi), vol. 3, no. 3, pp. 41–48, 2021, doi: https://doi.org/10.2005/jursistekni.v3i3.104.
W. F. Pickard, "Massive electricity storage for a developed economy of ten billion people," IEEE Access, vol. 3, pp. 1392–1407, 2015, doi: 10.1109/ACCESS.2015.2469255.
R. Bohte and L. Rossini, "Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models," J. Risk Financ. Manag., vol. 12, no. 3, p. 150, 2019, doi: 10.3390/jrfm12030150.
A. Jain, S. Tripathi, H. Dhardwivedi, and P. Saxena, "Forecasting Price of Cryptocurrencies Using Tweets Sentiment Analysis," 2018 11th Int. Conf. Contemp. Comput. IC3 2018, pp. 2–4, 2018, doi: 10.1109/IC3.2018.8530659.
N. A. Hitam, A. R. Ismail, and F. Saeed, "An Optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) for Cryptocurrency Forecasting," Procedia Comput. Sci., vol. 163, pp. 427–433, 2019, doi: 10.1016/j.procs.2019.12.125.
H. Sebastião and P. Godinho, "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financ. Innov., vol. 7, no. 1, 2021, doi: 10.1186/s40854-020-00217-x.
M. McCoy and S. Rahimi, "Prediction of Highly Volatile Cryptocurrency Prices Using Social Media," Int. J. Comput. Intell. Appl., vol. 19, no. 4, 2020, doi: 10.1142/S146902682050025X.
C. Hotz-Behofsits, F. Huber, and T. O. Zörner, "Predicting crypto-currencies using sparse non-Gaussian state space models," J. Forecast., vol. 37, no. 6, pp. 627–640, 2018, doi: 10.1002/for.2524.
R. Robiyanto, Y. A. Susanto, and R. Ernayani, "Examining the day-of-the-week-effect and the-month-of-the-year-effect in cryptocurrency market," J. Keuang. dan Perbank., vol. 23, no. 3, pp. 361–375, 2019, doi: 10.26905/jkdp.v23i3.3005.
S. K. Nayak, S. C. Nayak, and S. Das, "Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach," FinTech, vol. 1, no. 1, pp. 47–62, 2021, doi: 10.3390/fintech1010004.
R. Chowdhury, M. A. Rahman, M. S. Rahman, and M. R. C. Mahdy, "An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning," Phys. A Stat. Mech. its Appl., vol. 551, pp. 1–38, 2020, doi: 10.1016/j.physa.2020.124569.
N. Uras, L. Marchesi, M. Marchesi, and R. Tonelli, "Forecasting Bitcoin closing price series using linear regression and neural networks models," PeerJ Comput. Sci., vol. 6, pp. 1–25, 2020, doi: 10.7717/peerj-cs.279.
N. Salwa, N. Tatsara, R. Amalia, and A. F. Zohra, "Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average)," J. Data Anal., vol. 1, no. 1, pp. 21–31, 2018, doi: 10.24815/jda.v1i1.11874.