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INTEGRATED OF WEB APPLICATION RSHINY FOR MARKOV CHAIN AND ITS APPLICATION TO THE DAILY CASES OF COVID-19 IN WEST SUMATERA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 4 (2023) · pp. 2397–2410

DOI
10.30598/barekengvol17iss4pp2397-2410
ISSN
2615-3017, 1978-7227
Published
2023-12-19
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Discrete-time of Markov chains, starting now referred to as Markov chains, have been widely used by previous researchers in predicting the phenomenon. The predictions were made by manual calculations and using separate software, including Maple, Matlab, and Microsoft Excel. The analysis takes a relatively long time, especially in calculating the number of transitions from each state. This research built an integrated R script for the Markov chain based on the web application RShiny to quickly, easily, and accurately predict a phenomenon. The Markov chain integrated R script is built via command-command to predict the day-n distribution with the n-step distribution and long-term probability using a stationary distribution. The RShiny web application built is limited to state two and three. The integrated web application RShiny for the Markov chain is used to predict the daily cases of COVID-19 in West Sumatra. Based on the analysis carried out in predicting the daily cases of COVID-19 in West Sumatra from March 26, 2020, to October 20, 2020, for the next three days and in the long term, the results show that there is a 51.2% probability of an increase in COVID-19 cases, a 43% probability that cases will decrease, and 5.8% chance of stagnant cases

Collaboration network of the authors

24 authors · 54 co-authorship links

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  • National Research and Innovation Agency
  • Affiliation unknown
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  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dianne Amor KusumaAffiliation unknown4
Christophorus SoetiknoAffiliation unknown3
Devi MunandarNational Research and Innovation Agency2
Mutik AlawiyahAffiliation unknown1
Salsabila Eka PutriAffiliation unknown1
Syifani AkmaliahAffiliation unknown1
Riza Andrian IbrahimAffiliation unknown1
Sri Adi WidodoAffiliation unknown1
Estiyan DwipriyokoAffiliation unknown1
Shabira A. AuliyazhafiraAffiliation unknown1
Fariza A. PutriAffiliation unknown1
Theresia S. NauliAffiliation unknown1
Aulia R. Al MadaniAffiliation unknown1
I Gede Nyoman Mindra JayaAffiliation unknown1
Annisa N. FalahAffiliation unknown1
Aulia Rahman Al MadaniAffiliation unknown1
Sandrina NajwaAffiliation unknown1
Gumgum DarmawanAffiliation unknown1
Ega SahertiAffiliation unknown1
Salwa Azzah ImtiyazAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">INTEGRATED OF WEB APPLICATION RSHINY FOR MARKOV CHAIN AND ITS APPLICATION TO THE DAILY CASES OF COVID-19 IN WEST SUMATERA</dc:title>
	<dc:creator>Monika, Putri</dc:creator>
	<dc:creator>Ruchjana, Budi Nurani</dc:creator>
	<dc:creator>Parmikanti, Kankan</dc:creator>
	<dc:creator>Abdullah, Atje Setiawan</dc:creator>
	<dc:description xml:lang="en-US">Discrete-time of Markov chains, starting now referred to as Markov chains, have been widely used by previous researchers in predicting the phenomenon. The predictions were made by manual calculations and using separate software, including Maple, Matlab, and Microsoft Excel. The analysis takes a relatively long time, especially in calculating the number of transitions from each state. This research built an integrated R script for the Markov chain based on the web application RShiny to quickly, easily, and accurately predict a phenomenon. The Markov chain integrated R script is built via command-command to predict the day-n distribution with the n-step distribution and long-term probability using a stationary distribution. The RShiny web application built is limited to state two and three. The integrated web application RShiny for the Markov chain is used to predict the daily cases of COVID-19 in West Sumatra. Based on the analysis carried out in predicting the daily cases of COVID-19 in West Sumatra from March 26, 2020, to October 20, 2020, for the next three days and in the long term, the results show that there is a 51.2% probability of an increase in COVID-19 cases, a 43% probability that cases will decrease, and 5.8% chance of stagnant cases</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-12-19</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10326</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss4pp2397-2410</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications; 2397-2410</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss4year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10326/7221</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Putri Monika, Budi Nurani Ruchjana, Kankan Parmikanti, Atje Setiawan Abdullah</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>