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ROBUST CLUSTERING OF COVID-19 PANDEMIC WORLDWIDE

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 2 (2022) · pp. 687–694

DOI
10.30598/barekengvol16iss2pp687-694
ISSN
2615-3017, 1978-7227
Published
2022-06-01
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

COVID-19 pandemic is described as the most challenging crisis that humans have faced since World War II. From December 2019 until August 2021 based on the dataset provided by WHO, globally 219 countries in the world are affected by this virus. There are 205.338.159 cases cumulative total and 4.333.094 death cumulative total caused by this virus. In this paper, the data of 219 countries are analyzed using a robust clustering method namely K-Medoids cluster analysis. Based on the result, 219 countries in the world can be divided into five clusters based on four COVID-19-related variables, i.e. the number of cases cumulative total, death cumulative total, positive cases per capita, and case fatality rate. The distribution of the countries in five clusters was as follows; the first cluster contained 48 countries, the second cluster contained 3 countries, the third and fourth clusters contained 16 and 89 countries respectively, and the last cluster contained 63 countries. The largest cluster is the fourth one, containing countries that form a cluster with a centroid below the world average, and the smallest cluster is the second cluster with the high cases in all attributes, consisting of the USA, India, and Brazil.

Collaboration network of the authors

40 authors · 105 co-authorship links · 1 across different organisations · showing the 40 most connected

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

  • Lampung University
  • Politeknik Negeri Malang
  • University of Brawijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Januarius MujiyantoAffiliation unknown6
Mursid SalehAffiliation unknown5
Djoko SutopoAffiliation unknown4
Issy YuliasriAffiliation unknown4
Abdurrahman FaridiAffiliation unknown3
Dwi Anggani Linggar BharatiAffiliation unknown2
Dwi RukminiAffiliation unknown2
Mudyawati KamaruddinAffiliation unknown1
Rasheed AhmadAffiliation unknown1
Dian KurniasariLampung University1
Zaenal MukhlisinAffiliation unknown1
Wamiliana WamilianaAffiliation unknown1
Wahyu Nur HidayatPoliteknik Negeri Malang1
Rahmana Ilmi HakimUniversity of Brawijaya1
Ellvina Pramitadewi PramitadewiAffiliation unknown1
Fauziyatur RohmahAffiliation unknown1
Aldhy Sigma Lintang PamungkasAffiliation unknown1
Heri CahyonoAffiliation unknown1
Moh. AsroriAffiliation unknown1
Dzulkifli AbdullahAffiliation unknown1
Arina YuliartiAffiliation unknown1
Trias SeptianingsihAffiliation unknown1
Abdul KhalimAffiliation unknown1
Evita Ardy PrabasiwiAffiliation unknown1
Retno ApriliyantiAffiliation unknown1
Rudi HartonoAffiliation unknown1
Rizqi Amalia FAffiliation unknown1
Miftakhul UlumAffiliation unknown1
Nuna Mustikawati DewiAffiliation unknown1
Abdul AyizAffiliation unknown1
Yuni Awalaturrohmah SolihahAffiliation unknown1
Sri Wuli FirtiatiAffiliation unknown1
Sekar Ranis Anggita PahleviAffiliation unknown1
Isti KristianiAffiliation unknown1
Anis AmperawatyAffiliation unknown1
Iis Siti AisyahAffiliation 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">ROBUST CLUSTERING OF COVID-19 PANDEMIC WORLDWIDE</dc:title>
	<dc:creator>Wibowo, Rizki Agung</dc:creator>
	<dc:creator>Nisa, Khoirin</dc:creator>
	<dc:creator>Venelia, Hilda</dc:creator>
	<dc:creator>Warsono, Warsono</dc:creator>
	<dc:description xml:lang="en-US">COVID-19 pandemic is described as the most challenging crisis that humans have faced since World War II. From December 2019 until August 2021 based on the dataset provided by WHO, globally 219 countries in the world are affected by this virus. There are 205.338.159 cases cumulative total and 4.333.094 death cumulative total caused by this virus. In this paper, the data of 219 countries are analyzed using a robust clustering method namely K-Medoids cluster analysis.&amp;nbsp; Based on the result, 219 countries in the world can be divided into five clusters based on four COVID-19-related variables, i.e. the number of cases cumulative total, death cumulative total, positive cases per capita, and case fatality rate. The distribution of the countries in five clusters was as follows; the first cluster contained 48 countries, the second cluster contained 3 countries, the third and fourth clusters contained 16 and 89 countries respectively, and the last cluster contained 63 countries. The largest cluster is the fourth one, containing countries that form a cluster with a centroid below the world average, and the smallest cluster is the second cluster with the high cases in all attributes, consisting of the USA, India, and Brazil.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-06-01</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/5377</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss2pp687-694</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 687-694</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss2year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5377/4499</dc:relation>
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