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BICLUSTERING APPLICATION IN INDONESIAN ECONOMIC AND PANDEMIC VULNERABILITY

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 4 (2022) · pp. 1453–1464

DOI
10.30598/barekengvol16iss4pp1453-1464
ISSN
2615-3017, 1978-7227
Published
2022-12-15
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Biclustering is an analytical tool to group data from two dimensions simultaneously. The analysis was first introduced by Hartigan (1972) and applied by Cheng and Church (2000) to the gene expression matrix. The Cheng and Church (CC) algorithm is a popular biclustering algorithm and has been widely applied outside the field of biological data in recent years. This algorithm application in economic and Covid-19 pandemic vulnerability cases is exciting and essential to do in order to get an overview of the spatial pattern and characteristics of the bicluster of economic and COVID-19 pandemic vulnerability in Indonesia. This study uses secondary data from some ministries. Forming a bicluster using the CC algorithm requires determining the delta threshold so that several types of delta thresholds are formed to choose the best (optimum) using the evaluation of the average value of mean square residue (MSR) to volume ratios. The similarity of the optimum bi-cluster with the other is also seen based on the Liu and Wang index values. The 0.01 delta threshold is chosen as the optimum threshold because it produces the smallest average value of MSR to volume ratios (0.00032). Based on Liu and Wang Index values, the optimum threshold has a similarity level below 50% with other types of delta thresholds, so the threshold is the best unique threshold. The optimum threshold resulted in six biclusters (six spatial patterns). Most regions in Indonesia (11 provinces) tend to have low economic and COVID-19 pandemic vulnerability in the first spatial pattern characteristic variables.

Collaboration network of the authors

28 authors · 76 co-authorship links · 1 across different organisations

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  • IPB University
  • Bandung Islamic University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anwar FitriantoAffiliation unknown3
Kusman SadikAffiliation unknown3
Aji Hamim WigenaAffiliation unknown2
Farit Mochamad AfendiAffiliation unknown2
Anik DjuraidahAffiliation unknown2
Bagus SartonoIPB University2
Enny KristianiIPB University1
Ujang SumarwanIPB University1
Lilik Noor YuliatiIPB University1
Lismayani UsmanAffiliation unknown1
Ismah IsmahAffiliation unknown1
Faula ArinaAffiliation unknown1
Utami SyafitriAffiliation unknown1
Anggita Rizky FadilahAffiliation unknown1
Embay RohaetiAffiliation unknown1
Sitti NurhalizaAffiliation unknown1
Budi SusetyoAffiliation unknown1
Satria June AdwendiAffiliation unknown1
Khairil Anwar NotodiputroBandung Islamic University1
Anang KurniaAffiliation unknown1
Agus Sofian Eka HidayatAffiliation unknown1
Dian KusumaningrumAffiliation unknown1
Nur HikmahAffiliation unknown1
I'lmisukma RisnawatiAffiliation unknown1
Lukmanul HakimAffiliation 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">BICLUSTERING APPLICATION IN INDONESIAN ECONOMIC AND PANDEMIC VULNERABILITY</dc:title>
	<dc:creator>Ningsih, Wiwik Andriyani Lestari</dc:creator>
	<dc:creator>Sumertajaya, I Made</dc:creator>
	<dc:creator>Saefuddin, Asep</dc:creator>
	<dc:description xml:lang="en-US">Biclustering is an analytical tool to group data from two dimensions simultaneously. The analysis was first introduced by Hartigan (1972) and applied by Cheng and Church (2000) to the gene expression matrix. The Cheng and Church (CC) algorithm is a popular biclustering algorithm and has been widely applied outside the field of biological data in recent years. This algorithm application in economic and Covid-19 pandemic vulnerability cases is exciting and essential to do in order to get an overview of the spatial pattern and characteristics of the bicluster of economic and COVID-19 pandemic vulnerability in Indonesia. This study uses secondary data from some ministries. Forming a bicluster using the CC algorithm requires determining the delta threshold so that several types of delta thresholds are formed to choose the best (optimum) using the evaluation of the average value of mean square residue (MSR) to volume ratios. The similarity of the optimum bi-cluster with the other is also seen based on the Liu and Wang index values. The 0.01 delta threshold is chosen as the optimum threshold because it produces the smallest average value of MSR to volume ratios (0.00032). Based on Liu and Wang Index values, the optimum threshold has a similarity level below 50% with other types of delta thresholds, so the threshold is the best unique threshold. The optimum threshold resulted in six biclusters (six spatial patterns). Most regions in Indonesia (11 provinces) tend to have low economic and COVID-19 pandemic vulnerability in the first spatial pattern characteristic variables.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-12-15</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/6643</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss4pp1453-1464</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications; 1453-1464</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss4year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6643/5160</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Wiwik Andriyani Lestari Ningsih, I Made Sumertajaya, Asep Saefuddin</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>