C19-SmartQ: Applying Real-Time Multi-Organization Queuing Management System Using Predictive Model to Maintain Social Distancing

C19-SmartQ: Applying Real-Time Multi-Organization Queuing Management System Using Predictive Model to Maintain Social Distancing

COVID-19

queuing process

queue management system

predictive model

social distancing

COVID-19 is a pandemic crisis that has introduced new norm to the world where we are not encouraged to be in 3C areas, namely crowded place, confined space, and close conservation. We must also ensure that we are at least one meter apart from one another at all time even while queuing. The queuing process can be seen at any organization that offer services. Adhering to the new norm can be a challenge for organization such as banks, hospitals, and government offices when the number of clients waiting in queue increases while in confined space.  On the client’s side, they must go through the queue process of obtaining a queue number ticket and then wait to be served in confined and sometimes crowded space every time they require a service.  Thequeue process will be repeated at different premise. This study proposes real-time multi-organizationsC19-SmartQ system which use predictive modelling to generate single or consecutive queue number tickets for any client requiring services from two different organizations located within the same building.  C19-SmartQsystemmanages queues thus administer social distancing and streamline queues to reduce waiting periods and improve service efficiency. To ensure operability of C19-SmartQ system, itwas tested on the functionality and web server speed performance. The web server speed performance results show that data transfer and web loading were stable since there was only an increase of 0.2 seconds or 0.08% as the number of users per session increases. In the future, the system can be designed to accommodate queuing for more organizations located within the same building.  Machine learning can also be integrated in the system to improve the predictive modelling based on current environment at each organization.

Abdul Halim, Syafnidar

Othman, Mohd Hikmi

Buja, Alya Geogiana

Abdul Rahid, Nurul Najwa

Sharip, Anis Afiqah

Md Zain, Siti Maisarah

International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 06 (2021); pp. 108-123

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2021-03-30

Copyright (c) 2021 Syafnidar Abdul Halim, Mohd Hikmi Othman, Alya Geogiana Buja, Nurul Najwa Abdul Rahid, Anis Afiqah Sharip, Siti Maisarah Md Zain

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article