TY - GEN
T1 - Fuzzy Unsupervised Approaches to Analyze Covid-19 Spread for School Reopening Decision Making
AU - Muqtadiroh, Feby Artwodini
AU - Purwitasari, DIana
AU - Yuniarno, Eko Mulyanto
AU - Mardi Susiki Nugroho, Supeno
AU - Purnomo, Mauridhi Hery
AU - Pribadi Subriadi, Apol
AU - Rachmayanti, Riris DIana
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/10/13
Y1 - 2021/10/13
N2 - 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.
AB - 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.
KW - Artificial Intelligent
KW - Fuzzy
KW - K-Means
KW - RMSLE
KW - Readiness
KW - SEIR
KW - School Reopening
UR - http://www.scopus.com/inward/record.url?scp=85119514386&partnerID=8YFLogxK
U2 - 10.1109/IECON48115.2021.9589699
DO - 10.1109/IECON48115.2021.9589699
M3 - Conference contribution
AN - SCOPUS:85119514386
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021
Y2 - 13 October 2021 through 16 October 2021
ER -