TY - GEN
T1 - An Interactive Dashboard to Support Policymaking of Reopening School on Covid-19 Pandemic
AU - Muqtadiroh, Feby Artwodini
AU - Purwitasari, Diana
AU - Pahlawan, Muhammad Reza
AU - Yuniarno, Eko Mulyanto
AU - Nugroho, Supeno Mardi Susiki
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In mid-April 2020, UNESCO monitored 191 countries and stated that around 1.723 trillion students in the world were affected by the policy of school from home. It is feared that school closures could hamper the provision of education services and could disrupt the education process which will affect the level of quality of education. There is still no creation of a computational model for the spread of covid as the main framework for schools reopening safely during the pandemic situation. Although there is already a framework from WHO and the government, there is no measuring tool that can evaluate the effect of reopening schools while the Covid-19 pandemic. For this reason, this research seeks to produce a model for the spread of Covid-19 as a basis for determining policies for safely reopening schools during the pandemic. In this research, we produced a recommendation to reopen face-to-face learning in the form of a dashboard. Recommendations are given by predicting the number of cases in each subdistrict using a predictive model. The prediction results are also combined with the factors that have been determined by the government to give recommendations. The allotment of recommendations process involves a critical factor analysis process where we identify which factors are dominant as a basis of a controllable pandemic.
AB - In mid-April 2020, UNESCO monitored 191 countries and stated that around 1.723 trillion students in the world were affected by the policy of school from home. It is feared that school closures could hamper the provision of education services and could disrupt the education process which will affect the level of quality of education. There is still no creation of a computational model for the spread of covid as the main framework for schools reopening safely during the pandemic situation. Although there is already a framework from WHO and the government, there is no measuring tool that can evaluate the effect of reopening schools while the Covid-19 pandemic. For this reason, this research seeks to produce a model for the spread of Covid-19 as a basis for determining policies for safely reopening schools during the pandemic. In this research, we produced a recommendation to reopen face-to-face learning in the form of a dashboard. Recommendations are given by predicting the number of cases in each subdistrict using a predictive model. The prediction results are also combined with the factors that have been determined by the government to give recommendations. The allotment of recommendations process involves a critical factor analysis process where we identify which factors are dominant as a basis of a controllable pandemic.
KW - clustering
KW - covid-19
KW - dashboard
KW - decision making
KW - predictive model
KW - school reopen
UR - http://www.scopus.com/inward/record.url?scp=85137831861&partnerID=8YFLogxK
U2 - 10.1109/CIVEMSA53371.2022.9853645
DO - 10.1109/CIVEMSA53371.2022.9853645
M3 - Conference contribution
AN - SCOPUS:85137831861
T3 - CIVEMSA 2022 - IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings
BT - CIVEMSA 2022 - IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2022
Y2 - 15 June 2022 through 17 June 2022
ER -