Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.

J. Taskinsoy, “Bitcoin: The Longest Running Mania–Tulips of the 21st Century?,” Available SSRN 3505953, 2019.

J. Mattke, C. Maier, L. Reis, and T. Weitzel, “Bitcoin investment: a mixed methods study of investment motivations,” Eur. J. Inf. Syst., vol. 30, no. 3, pp. 261–285, 2021, doi: https://doi.org/10.1080/0960085X.2020.1787109.

S. Nakamoto and A. Bitcoin, “A peer-to-peer electronic cash system,” Bitcoin.–URL https//bitcoin. org/bitcoin. pdf, vol. 4, p. 2, 2008.

B. A. Kurniawan, “Peramalan Harga BitCoin Menggunakan Back-Propagation Neural Network.” Institut Teknologi Sepuluh Nopember, 2018.

D. Guegan, “The Digital World: I-Bitcoin: from history to real live,” 2018.

T. Shintate and L. Pichl, “Trend prediction classification for high frequency bitcoin time series with deep learning,” J. Risk Financ. Manag., vol. 12, no. 1, p. 17, 2019.

Y. B. Wijaya, S. Kom, and T. A. Napitupulu, “Stock price prediction: comparison of Arima and artificial neural network methods-An Indonesia Stock’s Case,” in 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2010, pp. 176–179.

A. Lama, K. N. Singh, B. Gurung, and S. Rathod, “Parameter estimation of time series models using Bayesian technique,” 2021.

S. McNally, “Predicting the price of Bitcoin using Machine Learning.” Dublin, National College of Ireland, 2016.

M. J. S. de Souza et al., “Can artificial intelligence enhance the Bitcoin bonanza,” J. Financ. Data Sci., vol. 5, no. 2, pp. 83–98, 2019.

Z. Chen, C. Li, and W. Sun, “Bitcoin price prediction using machine learning: An approach to sample dimension engineering,” J. Comput. Appl. Math., vol. 365, p. 112395, 2020.

G. Serafini et al., “Sentiment-driven price prediction of the bitcoin based on statistical and deep learning approaches,” in 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–8.

M. Poongodi, V. Vijayakumar, and N. Chilamkurti, “Bitcoin price prediction using ARIMA model,” Int. J. Internet Technol. Secur. Trans., vol. 10, no. 4, pp. 396–406, 2020.

A. H. A. Othman, S. Kassim, R. Bin Rosman, and N. H. B. Redzuan, “Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach,” J. Revenue Pricing Manag., vol. 19, no. 5, pp. 314–330, 2020.

J. Kamruzzaman and R. A. Sarker, “ANN-based forecasting of foreign currency exchange rates,” Neural Inf. Process. Rev., vol. 3, no. 2, pp. 49–58, 2004.

Z. Wang, Y. Liu, and P. J. Griffin, “A combined ANN and expert system tool for transformer fault diagnosis,” in 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No. 00CH37077), 2000, vol. 2, pp. 1261–1269.

K. Abhishek, A. Kumar, R. Ranjan, and S. Kumar, “A rainfall prediction model using artificial neural network,” in 2012 IEEE Control and System Graduate Research Colloquium, 2012, pp. 82–87.

A. Weigend, “On overfitting and the effective number of hidden units,” in Proceedings of the 1993 connectionist models summer school, 1994, vol. 1, pp. 335–342.

C. J. Lin, T.-L. Hsieh, C.-W. Yang, and R.-J. Huang, “The impact of computer-based procedures on team performance, communication, and situation awareness,” Int. J. Ind. Ergon., vol. 51, pp. 21–29, 2016.

M. Castro-Neto, Y.-S. Jeong, M.-K. Jeong, and L. D. Han, “Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions,” Expert Syst. Appl., vol. 36, no. 3, pp. 6164–6173, 2009.

F. Zhang, C. Deb, S. E. Lee, J. Yang, and K. W. Shah, “Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique,” Energy Build., vol. 126, pp. 94–103, 2016.

E. Levin, R. Pieraccini, and W. Eckert, “A stochastic model of human-machine interaction for learning dialog strategies,” IEEE Trans. speech audio Process., vol. 8, no. 1, pp. 11–23, 2000.

A. J. Thomas, M. Petridis, S. D. Walters, S. M. Gheytassi, and R. E. Morgan, “Two hidden layers are usually better than one,” in Engineering Applications of Neural Networks: 18th International Conference, EANN 2017, Athens, Greece, August 25–27, 2017, Proceedings, 2017, pp. 279–290.