Designing an Artificial Intelligence using Fuzzy Logic for Assessing COVID-19 Risks in Higher Education Institutions during In-Person Class Resumption

Authors

  • Jose Candia Jr Northeastern Mindanao State University – Tagbina Campus, Surigao del Sur, Philippines 8308
  • Ike Gonzales Northeastern Mindanao State University – Tagbina Campus, Surigao del Sur, Philippines 8308
  • Joenil Frayco Northeastern Mindanao State University – Tagbina Campus, Surigao del Sur, Philippines 8308
  • Cristy Lou Jabel Northeastern Mindanao State University – Tagbina Campus, Surigao del Sur, Philippines 8308
  • Ariston Ronquillo Northeastern Mindanao State University – Tagbina Campus, Surigao del Sur, Philippines 8308
  • Zarina Gail D. Sambalod Northeastern Mindanao State University – Tagbina Campus, Surigao del Sur, Philippines 8308

Keywords:

Covid-19 risk assessment, School re-opening, Fuzzy logic model developmen, Face-to-face class

Abstract

The COVID-19 pandemic has had a significant impact on the education sector, leading to the closure of schools to prevent the spread of the virus. With the Philippine government approving the reopening of face-to-face classes in colleges and universities, there is a need to ensure that the academic community is protected from the risks associated with COVID-19. This study developed a Fuzzy Logic-based model to measure the risk associated with COVID-19 transmission in Northeastern Mindanao State University - Tagbina campus.  The research design employed in this study involved the development of the Fuzzy Logic-based model, which was validated by experts in the field to assess COVID-19 risk transmission. The developed model produced satisfactory results after expert validation, and the campus had a 38.5% risk, classified as "Low," based on the developed model. Despite challenges in opinions of multiple experts, the model was able to draw conclusions to support campus management’s decision-making pertaining to campus risk of COVID-19 transmission. The developed model can be used as a decision-support tool for campus administration to implement certain modalities and policies that do not pose a high COVID-19 risk to the academic community. Further studies can explore the applicability of the developed model to other higher education institutions and settings.

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Published

2023-12-28

How to Cite

Candia Jr, J., Gonzales, . I. ., Frayco, J. ., Jabel, C. L. ., Ronquillo, A., & Sambalod, Z. G. D. . (2023). Designing an Artificial Intelligence using Fuzzy Logic for Assessing COVID-19 Risks in Higher Education Institutions during In-Person Class Resumption. Journal of Multidisciplinary in Social Sciences, 19(3), 16–22. Retrieved from https://so03.tci-thaijo.org/index.php/sduhs/article/view/274224

Issue

Vol. 19 No. 3 (2023): September - December

Section

Original Articles

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