Long Short Term Memory on Bitcoin Price Forecasting

Tuti Purwaningsih, Gita Evi Kusumandari

Abstract


In modern times, many people rely on sophisticated technology to meet their needs. Already many technologies today can replace the role and function of society in the field of investment. There are many ways to fulfill the lives of these people, such as Bitcoin investment. Bitcoin is a digital asset that only exists in digital form by means of peer-to-peer work. To maximize profits, it is necessary to forecast Bitcoin prices when it will go up or down. This study tries to address the changes in Bitcoin prices whether to go up or down the next day with an artificial neural network model. The editor used in this study is the LSTM method. The data used is the Bitcoin blockchain data, namely time-series data in a one-day period from 1 January 2018 to 31 May 2019. Obtained forecasting results in June 2019 for Bitcoin to rise slowly and an accuracy value of 97.5% based on MAPE with the first day worth $8901.50.

Keywords


Bitcoin; LSTM; Forecasting; MAPE

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References


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DOI: https://doi.org/10.12928/mf.v3i1.3857

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Copyright (c) 2021 Tuti Purwaningsih, Gita Evi Kusumandari

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Mobile and Forensics (MF)

ISSN Online: 2714-6685 | Print: 2656-6257
Organized by Department of Magister Teknik Informatika
Published by Universitas Ahmad Dahlan 
Website : http://journal2.uad.ac.id/index.php/mf 
Email 1 : mf.mti@uad.ac.id
Email 2 : ahmad.azhari@tif.uad.ac.id


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