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Analysis of Covid 19 Data in Indonesia Using Supervised Emerging Patterns

APTISI Transactions on Management (ATM) · Vol. 6 No. 1 (2022) · hlm. 91–101

DOI
10.33050/atm.v6i1.1768
ISSN
2622-6804, 2622-6812
Terbit
2022-01-03
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This research method uses CRISP-DM with emerging pattern supervision modeling and EPM Algorithm. The contribution of the research is to assist the Government in overcoming the problem of the spread of the COVID-19 cluster in several regions in Indonesia. The research aims to implement information on the COVID-19 data mining pattern in the DKI Jakarta area. The problems faced are the difficulty of identifying the pattern of COVID-19 data in one area, it is difficult to dig up data on the http://corona.jakarta.go.id website. It is not easy to decide on the handling of COVID-19. The output of the research results in a cluster of information on COVID-19 in the DKI Jakarta area based on Significance level depends on the Covid Map In terms of Region, Status, Gender, & age And Signification can be the basis for determining covid OTG, DTG, and Positive. The theoretical and practical implications can be stated as follows: The use of supervised emerging pattern methods can affect the processing of COVID-19 data. For 5 Regions in DKI Jakarta and distribution to determine covid OTG, DTG, and Positive. The result of the development of this data mining system is to produce pattern reports to produce Supervised Emerging Patterns technology for decision making at the COVID-19 Task Force in DKI Jakarta.

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  • Universitas Raharja
  • Satya Wacana Christian University
  • Multimedia Nusantara University
  • Universitas Catur Insan Cendekia Cirebon
  • Universitas Pradita
  • Afiliasi tidak diketahui
  • Organisasi berbeda
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Rekan penulisOrganisasiArtikel bersama
Qurotul AiniUniversitas Raharja19
Ninda LutfianiUniversitas Raharja9
Irwan SembiringSatya Wacana Christian University6
Danny ManonggaSatya Wacana Christian University6
Po Abas SunaryaAfiliasi tidak diketahui4
Ihsan Nuril HikamAfiliasi tidak diketahui4
Alfiah KhoirunisaUniversitas Raharja3
Dwi ApriliasariAfiliasi tidak diketahui3
Muhtarom MuhtaromAfiliasi tidak diketahui3
Agung RizkyAfiliasi tidak diketahui3
Sutarto WijonoAfiliasi tidak diketahui3
Shofiyul MillahAfiliasi tidak diketahui3
Nuke Puji Lestari SantosoUniversitas Raharja3
Dwi JulianingsihAfiliasi tidak diketahui3
Tuti NurhaeniAfiliasi tidak diketahui2
Desy AprianiAfiliasi tidak diketahui2
Alfri AdiwijayaAfiliasi tidak diketahui2
Mohamad RakhmansyahAfiliasi tidak diketahui2
Adam FaturahmanAfiliasi tidak diketahui2
Natasya Aprila YusufAfiliasi tidak diketahui2
Umi NarimawatiAfiliasi tidak diketahui2
Joko SiswantoAfiliasi tidak diketahui2
Hindriyanto Dwi PurnomoSatya Wacana Christian University2
Nuke Puji Lestari SantosoAfiliasi tidak diketahui2
Aditya MardiansyahAfiliasi tidak diketahui2
Nanda SeptianiAfiliasi tidak diketahui2
Aan KaniviaAfiliasi tidak diketahui2
Muhammad Faizal FazriAfiliasi tidak diketahui2
Sabda MaulanaAfiliasi tidak diketahui2
Chandra LukitaUniversitas Catur Insan Cendekia Cirebon2
Muhamad YusupAfiliasi tidak diketahui2
Maulana Arif KomaraAfiliasi tidak diketahui2
I Ketut Adi WirayasaUniversitas Pradita1
Raihan RaihanAfiliasi tidak diketahui1
Goh YaoAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://ijc.ilearning.co/ATM/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Analysis of Covid 19 Data in Indonesia Using Supervised Emerging Patterns </dc:title>
	<dc:creator xml:lang="en">Rahardja, Untung</dc:creator>
	<dc:creator xml:lang="en">Dewanto, Ignatius Joko</dc:creator>
	<dc:creator xml:lang="en">Djajadi, Arko</dc:creator>
	<dc:creator xml:lang="en">Candra, Ariya Panndhitthana</dc:creator>
	<dc:creator xml:lang="en">Hardini, Marviola</dc:creator>
	<dc:creator xml:lang="id">Hardini, Marviola</dc:creator>
	<dc:subject xml:lang="en">CRISP-DM</dc:subject>
	<dc:subject xml:lang="en">Emerging Pattern Mining Algorithm</dc:subject>
	<dc:subject xml:lang="en">COVID-19</dc:subject>
	<dc:subject xml:lang="en">Indonesia</dc:subject>
	<dc:description xml:lang="en">This research method uses CRISP-DM with emerging pattern supervision modeling and EPM Algorithm. The contribution of the research is to assist the Government in overcoming the problem of the spread of the COVID-19 cluster in several regions in Indonesia. The research aims to implement information on the COVID-19 data mining pattern in the DKI Jakarta area. The problems faced are the difficulty of identifying the pattern of COVID-19 data in one area, it is difficult to dig up data on the http://corona.jakarta.go.id website. It is not easy to decide on the handling of COVID-19. The output of the research results in a cluster of information on COVID-19 in the DKI Jakarta area based on Significance level depends on the Covid Map In terms of Region, Status, Gender, &amp;amp; age And Signification can be the basis for determining covid OTG, DTG, and Positive. The theoretical and practical implications can be stated as follows: The use of supervised emerging pattern methods can affect the processing of COVID-19 data. For 5 Regions in DKI Jakarta and distribution to determine covid OTG, DTG, and Positive. The result of the development of this data mining system is to produce pattern reports to produce Supervised Emerging Patterns technology for decision making at the COVID-19 Task Force in DKI Jakarta.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2022-01-03</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://ijc.ilearning.co/ATM/article/view/1768</dc:identifier>
	<dc:identifier>10.33050/atm.v6i1.1768</dc:identifier>
	<dc:source xml:lang="en">APTISI Transactions on Management (ATM); Vol. 6 No. 1 (2022): January; 91-101</dc:source>
	<dc:source xml:lang="id">APTISI Transactions on Management (ATM); Vol 6 No 1 (2022): January; 91-101</dc:source>
	<dc:source>2622-6804</dc:source>
	<dc:source>2622-6812</dc:source>
	<dc:source>10.33050/atm.v6i1</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://ijc.ilearning.co/ATM/article/view/1768/570</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Untung Rahardja, Ignatius Joko Dewanto, Arko Djajadi, Ariya Panndhitthana Candra, Marviola Hardini (Author)</dc:rights>
	<dc:rights xml:lang="en">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>