Analysis of Covid 19 Data in Indonesia Using Supervised Emerging Patterns


  • Untung Rahardja Universiti Teknologi Malaysia
  • Ignatius Joko Dewanto University of Raharja
  • Arko Djajadi University of Raharja
  • Ariya Panndhitthana Candra University of Raharja
  • Marviola Hardini University of Raharja



CRISP-DM, Emerging Pattern Mining Algorithm, COVID-19, Indonesia


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 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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How to Cite

Rahardja, U., Dewanto, I. J., Djajadi, A., Candra, A. P., & Hardini, M. (2022). Analysis of Covid 19 Data in Indonesia Using Supervised Emerging Patterns . APTISI Transactions on Management (ATM), 6(1), 91–101.

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