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All Journal Aptisi Transactions on Management
Arko Djajadi
University of Raharja
Computer Science & IT Decision Sciences, Operations Research & Management

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Analysis of Covid 19 Data in Indonesia Using Supervised Emerging Patterns Untung Rahardja; Ignatius Joko Dewanto; Arko Djajadi; Ariya Panndhitthana Candra; Marviola Hardini
APTISI Transactions on Management (ATM) Vol 6 No 1 (2022): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v6i1.1768

Abstract

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.
Co-Authors Ariya Panndhitthana Candra Ignatius Joko Dewanto Marviola Hardini Rahardja, Untung