Melola Discovery

← Melola Discovery

Open access Journal

Peramalan Cryptocurrency dengan Autoregressive Integrated Moving Average (ARIMA) dan Risiko Kerugian dengan Value at Risk (VaR)

Journal of Science and Applicative Technology · Vol. 6 No. 2 (2022)

DOI
10.35472/jsat.v6i2.904
ISSN
2581-0545
Published
2022-12-29
Publisher
Institut Teknologi Sumatra
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Blockchain is a technology that is used for recording digital transactions that are interconnected and cannot be changed. Cryptocurrencies use blockchain technology, which has advantages due to a high level of security, low fees, and a high return on investment. One of the most popular cryptocurrencies and one that has a high market cap is Bitcoin. High volatility carries the risk of large losses. So it is necessary to analyze the risk of loss and forecast Bitcoin. Forecasting is carried out using the Autoregressive Integrated Moving Average (ARIMA) model, which is then carried out by risk analysis using Value at Risk (VaR) using the Historical Data method. Based on the research results, ARIMA [4,1,2] was great for predicting Bitcoin, with a Mean Absolute Percentage Error (MAPE) of 6%. Based on the results of research with Value at Risk (VaR), investors have a maximum loss tolerance of 5.86% and there is a 5% possibility that the losses will exceed 5.85%.

Collaboration network of the authors

8 authors · 12 co-authorship links

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

  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Tiara YulitaAffiliation unknown2
Dian AnggrainiAffiliation unknown1
Andi FitriawatiAffiliation unknown1
Radot MH SiahaanAffiliation unknown1
Cindy FaradillaAffiliation unknown1
Chintya Carissa ManurungAffiliation 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.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2581-0545/works, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Peramalan Cryptocurrency dengan Autoregressive Integrated Moving Average (ARIMA) dan Risiko Kerugian dengan Value at Risk (VaR)</dc:title><dc:creator>Listiani, Amalia</dc:creator><dc:creator>Mahkya, Dani Al</dc:creator><dc:description>&lt;jats:p&gt;Blockchain is a technology that is used for recording digital transactions that are interconnected and cannot be changed. Cryptocurrencies use blockchain technology, which has advantages due to a high level of security, low fees, and a high return on investment. One of the most popular cryptocurrencies and one that has a high market cap is Bitcoin. High volatility carries the risk of large losses. So it is necessary to analyze the risk of loss and forecast Bitcoin. Forecasting is carried out using the Autoregressive Integrated Moving Average (ARIMA) model, which is then carried out by risk analysis using Value at Risk (VaR) using the Historical Data method. Based on the research results, ARIMA [4,1,2] was great for predicting Bitcoin, with a Mean Absolute Percentage Error (MAPE) of 6%. Based on the results of research with Value at Risk (VaR), investors have a maximum loss tolerance of 5.86% and there is a 5% possibility that the losses will exceed 5.85%.&lt;/jats:p&gt;</dc:description><dc:publisher>Institut Teknologi Sumatra</dc:publisher><dc:date>2022-12-29</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.35472/jsat.v6i2.904</dc:identifier><dc:identifier>10.35472/jsat.v6i2.904</dc:identifier><dc:source>Journal of Science and Applicative Technology; Vol 6 No 2 (2022); 85</dc:source><dc:source>2581-0545</dc:source><dc:relation>https://journal.itera.ac.id/index.php/jsat/article/download/904/344</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>