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

Authors

  • Syafnidar Abdul Halim Universiti Teknologi MARA
  • Mohd Hikmi Othman Cawangan Melaka Kampus Jasin, Malaysia
  • Alya Geogiana Buja Cawangan Melaka Kampus Jasin, Malaysia
  • Nurul Najwa Abdul Rahid Cawangan Melaka Kampus Jasin, Malaysia
  • Anis Afiqah Sharip Cawangan Melaka Kampus Jasin, Malaysia
  • Siti Maisarah Md Zain Cawangan Melaka Kampus Jasin, Malaysia

DOI:

https://doi.org/10.3991/ijim.v15i06.20597

Keywords:

COVID-19, queuing process, queue management system, predictive model, social distancing

Abstract


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.

Author Biographies

Syafnidar Abdul Halim, Universiti Teknologi MARA

Cawangan Melaka Kampus Jasin, Malaysia

Mohd Hikmi Othman, Cawangan Melaka Kampus Jasin, Malaysia

Universiti Teknologi MARA,

Alya Geogiana Buja, Cawangan Melaka Kampus Jasin, Malaysia

Universiti Teknologi MARA,

Nurul Najwa Abdul Rahid, Cawangan Melaka Kampus Jasin, Malaysia

Universiti Teknologi MARA,

Anis Afiqah Sharip, Cawangan Melaka Kampus Jasin, Malaysia

Universiti Teknologi MARA,

Siti Maisarah Md Zain, Cawangan Melaka Kampus Jasin, Malaysia

Universiti Teknologi MARA,

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Published

2021-03-30

How to Cite

Abdul Halim, S., Othman, M. H., Buja, A. G., Abdul Rahid, N. N., Sharip, A. A., & Md Zain, S. M. (2021). C19-SmartQ: Applying Real-Time Multi-Organization Queuing Management System Using Predictive Model to Maintain Social Distancing. International Journal of Interactive Mobile Technologies (iJIM), 15(06), pp. 108–123. https://doi.org/10.3991/ijim.v15i06.20597

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Section

Papers