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Modeling of COVID-19 Cases in Indonesia with the Method of Geographically Weighted Regression

Jurnal Matematika, Statistika dan Komputasi · Vol. 19 No. 2 (2023) · pp. 342–350

DOI
10.20956/j.v19i2.23481
ISSN
2614-8811, 1858-1382
Published
2023-01-05
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

The COVID-19 pandemic has spread to all corners of the world, including Indonesia. Various factors affect the spread of COVID-19 cases in an area so that the government and the community can make prevention and control efforts so that this pandemic does not spread. This study aims to model the number of COVID-19 cases in Indonesia using the Geographically Weighted Regression (GWR) method, which develops a linear regression model. The GWR model uses weights based on the location of each observation so that the model is obtained for that location. Determine the weighting on the bandwidth. Optimum bandwidth selection is obtained by minimizing the value of Cross-Validation (CV). The GWR model using a fixed bisquare kernel weighting function has an optimum bandwidth of 0.999948 with a minimum CV value of 397.076.128 with a coefficient of determination R2 of 85.1 %. The results show that the number of positive cases positively correlates with the number of patients who died from COVID-19. In contrast, the number of recovered patients negatively correlates with the number of patients who died from COVID-19.

Keywords

Collaboration network of the authors

40 authors · 94 co-authorship links · 4 across different organisations

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

  • Hasanuddin University
  • Binus University
  • Universitas Islam Negeri Salatiga
  • Lampung University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Georgina Maria TinungkiHasanuddin University5
Nasrah SirajangHasanuddin University4
Nurtiti SunusiHasanuddin University3
Saleh AfAffiliation unknown2
Anton BawonoUniversitas Islam Negeri Salatiga1
Anna IslamiyatiHasanuddin University1
Raupong RaupongHasanuddin University1
Rizki AdiputraHasanuddin University1
Sitti SahrimanHasanuddin University1
Ika FitrianaHasanuddin University1
Ratmila MammiAffiliation unknown1
Aqilah Salsabila RahmanAffiliation unknown1
Riska RasyidHasanuddin University1
Nurhidaya LAffiliation unknown1
Sonya SonyaAffiliation unknown1
Sitti SahrimanAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Nuralyatussa’ AdaAffiliation unknown1
Alimatun NajihaHasanuddin University1
I Gusti Agung Anom YudistiraBinus University1
Rinda NariswariBinus University1
Andi Kresna JayaHasanuddin University1
Fadhila Febriyanti NajamuddinHasanuddin University1
Mayashari MayashariHasanuddin University1
Anisa AnisaHasanuddin University1
Mustabsyirah MustabsyirahHasanuddin University1
Agung Muhammad TakdirHasanuddin University1
Sri Astuti ThamrinHasanuddin University1
Nuravia NuraviaAffiliation unknown1
Fitriani FitrianiLampung University1
Andi Fitri AyuAffiliation unknown1
Aidawayati RangkutiHasanuddin University1
Andi waru A. PaluseriAffiliation unknown1
Nirwan IlyasAffiliation unknown1
Anisa AnisaHasanuddin University1
Irawaty IrawatyAffiliation unknown1
Suci Barlian SariHasanuddin University1
Puji Puspa SariHasanuddin University1

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.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Modeling of COVID-19 Cases in Indonesia with the Method of Geographically Weighted Regression</dc:title>
	<dc:creator xml:lang="en">Arifin, Samsul </dc:creator>
	<dc:creator xml:lang="en">Herdiani, Erna Tri </dc:creator>
	<dc:subject xml:lang="en">Selected:COVID-19 Pandemic</dc:subject>
	<dc:subject xml:lang="en">Geographically Weighted Regression</dc:subject>
	<dc:subject xml:lang="en">Cross-Validation</dc:subject>
	<dc:subject xml:lang="en">Coefficient of Determination</dc:subject>
	<dc:description xml:lang="en">The COVID-19 pandemic has spread to all corners of the world, including Indonesia. Various factors affect the spread of COVID-19 cases in an area so that the government and the community can make prevention and control efforts so that this pandemic does not spread. This study aims to model the number of COVID-19 cases in Indonesia using the Geographically Weighted Regression (GWR) method, which develops a linear regression model. The GWR model uses weights based on the location of each observation so that the model is obtained for that location. Determine the weighting on the bandwidth. Optimum bandwidth selection is obtained by minimizing the value of Cross-Validation (CV). The GWR model using a fixed bisquare kernel weighting function has an optimum bandwidth of 0.999948 with a minimum CV value of 397.076.128 with a coefficient of determination R2   of 85.1 %. The results show that the number of positive cases positively correlates with the number of patients who died from COVID-19. In contrast, the number of recovered patients negatively correlates with the number of patients who died from COVID-19.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2023-01-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/23481</dc:identifier>
	<dc:identifier>10.20956/j.v19i2.23481</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 19 No. 2 (2023): JANUARY 2023; 342-350</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/23481/9157</dc:relation>
	<dc:relation>10.20956/j.v19i2.23481.g9157</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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