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Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 1 (2023) · pp. 33–44

DOI
10.22146/ijccs.80776
ISSN
2460-7258, 1978-1520
Published
2023-02-23
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

As cryptocurrencies develop, it cannot be denied that crypto prices are volatile. One of the influencing factors is the increasing volume of transactions which attracts the interest of researchers to conduct research in developing coin price predictions from cryptocurrencies. The method, algorithm and amount of data affect the prediction results. In this study, prediction modelling will be carried out using the LSTM method and short-term data. This study will conduct two experiments using the simple LSTM method and utilising multivariate time series with LSTM. The smallest predicted value is obtained using an 80/20 data allocation distribution scenario, input layer LSTM = 360, Epoch = 500, a Solana coin with RMSE = 0.111, R2 = 0.9962. It can be interpreted that short-term data can be used in making predictive models. Still, special attention needs to be paid to the characteristics of the dataset used and the modelling methodology, and it is hoped that the results of this study can be used in further research.

Keywords

Collaboration network of the authors

36 authors · 78 co-authorship links · 18 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Amikom Yogyakarta
  • Universitas Gadjah Mada
  • Universitas Muhammadiyah Buton
  • Universidad de Murcia
  • Hellenic Open University
  • Universitas Insan Mahardika
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ema UtamiUniversitas Amikom Yogyakarta3
Arief SetyantoUniversitas Amikom Yogyakarta3
I Made Artha AgastyaUniversitas Amikom Yogyakarta3
Rodrigo Martínez‐BéjarUniversidad de Murcia2
Ariel Yonatan AlinUniversitas Amikom Yogyakarta1
Kumara Ari YuanaUniversitas Amikom Yogyakarta1
Retzi Yosia LewuAffiliation unknown1
Ainul YaqinUniversitas Amikom Yogyakarta1
Agus HarjokoUniversitas Gadjah Mada1
Retantyo WardoyoUniversitas Gadjah Mada1
Sri T. HartatiUniversitas Gadjah Mada1
Andi SunyotoUniversitas Amikom Yogyakarta1
Ade PujiantoUniversitas Amikom Yogyakarta1
Mardi Siswo UtomoAffiliation unknown1
Hanif Al FattaUniversitas Amikom Yogyakarta1
Muhammad Resa Arif YudiantoUniversitas Amikom Yogyakarta1
Alba Puelles LópezUniversidad de Murcia1
Mohamad FirdausUniversitas Insan Mahardika1
Mitrakasih La Ode OndeUniversitas Muhammadiyah Buton1
Anisa RizkayatiUniversitas Muhammadiyah Buton1
Winda FebriantiUniversitas Muhammadiyah Buton1
Muhamad IksanAffiliation unknown1
La Aba AbaAffiliation unknown1
Pahrul PahrulAffiliation unknown1
Mera Kartika DelimayantiAffiliation unknown1
Mauldy LayaAffiliation unknown1
Muhammad Reza FaisalAffiliation unknown1
Kristina VaherAffiliation unknown1
Syamsu HidayatAffiliation unknown1
Agus PurwantoAffiliation unknown1
David AgustriawanAffiliation unknown1
Agung NugrohoAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Improved LSTM Method of Predicting Cryptocurrency Price Using Short-Term Data</dc:title>
	<dc:creator>Sari, Risna</dc:creator>
	<dc:creator>Kusrini, Kusrini</dc:creator>
	<dc:creator>Hidayat, Tonny</dc:creator>
	<dc:creator>Orphanoudakis, Theofanis</dc:creator>
	<dc:subject xml:lang="en-US">Machine Learning; Data Science; Prediction</dc:subject>
	<dc:subject xml:lang="en-US">Cryptocurrency; Prediction; Long Short-term Memory (LSTM); Short-term data</dc:subject>
	<dc:description xml:lang="en-US">As cryptocurrencies develop, it cannot be denied that crypto prices are volatile. One of the influencing factors is the increasing volume of transactions which attracts the interest of researchers to conduct research in developing coin price predictions from cryptocurrencies. The method, algorithm and amount of data affect the prediction results. In this study, prediction modelling will be carried out using the LSTM method and short-term data. This study will conduct two experiments using the simple LSTM method and utilising multivariate time series with LSTM. The smallest predicted value is obtained using an 80/20 data allocation distribution scenario, input layer LSTM = 360, Epoch = 500, a Solana coin with RMSE = 0.111, R2 = 0.9962. It can be interpreted that short-term data can be used in making predictive models. Still, special attention needs to be paid to the characteristics of the dataset used and the modelling methodology, and it is hoped that the results of this study can be used in further research.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-02-23</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/80776</dc:identifier>
	<dc:identifier>10.22146/ijccs.80776</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 1 (2023): January; 33-44</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/80776/35779</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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