Virus SARS-Cov-2 causing Covid-19 spreads quickly and brings high risks to transmissions. The government to rule strictly to arrange strategies to minimize interactions through School-From-Home (SFH) policy. Unfortunately, the school closure is the potential to hamper deliveries of education services and may entail destructive impacts to quality education performance. There must be a consideration to school reopen safely during the pandemic.The objective of the research is to produce a model of Covid-19 spreads to analyze the readiness of school to reopen. This study adopts a SEIR model to predict the spread of Covid-19 using dataset from 23 March through 31 December 2020. The best model is selected from the one having the least error and adopted to predict the spread in the next 100 days starting from 01 January 2021 through 10 April 2021.Clustering was then implemented to acquire the character's proximity in each area using K-Means algorithm. While unsupervised fuzzy was picked out to seize the phenomenon of the dynamic as Covid-19 spread as a basis to decision making on school reopen safely during the pandemic. These whole concepts will serve the decision making effectively and intelligently by generating a better estimation.This study resulted in a Covid-19 spread model with an average error of 0.2% based on the RMSLE calculation.

Original languageEnglish
Title of host publicationIECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
ISBN (Electronic)9781665435543
Publication statusPublished - 13 Oct 2021
Event47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 - Toronto, Canada
Duration: 13 Oct 202116 Oct 2021

Publication series

NameIECON Proceedings (Industrial Electronics Conference)


Conference47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021


  • Artificial Intelligent
  • Fuzzy
  • K-Means
  • Readiness
  • SEIR
  • School Reopening


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