Analysis of the use of artificial neural network models in predicting bitcoin prices

Sahi, Muhammad, Faisal, Muhammad ORCID: https://orcid.org/0000-0003-4884-7254, Arif, Yunifa Miftachul and Crysdian, Cahyo ORCID: https://orcid.org/0000-0002-7488-6217 (2023) Analysis of the use of artificial neural network models in predicting bitcoin prices. Applied Information System and Management, 6 (2). pp. 91-96. ISSN 2621-2544

Preview

Text 15686.pdf - Published Version Available under License Creative Commons Attribution Share Alike. Download (444kB) | Preview

Abstract

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%.

Item Type:	Journal Article
Keywords:	artificial neural network; bitcoin; predicting
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Muhammad Faisal
Date Deposited:	02 Oct 2023 09:05

Downloads

Origin of downloads

Actions (login required)

View Item