Determination the Number and Schedule of Security Officers for Pandemic Period and Hybrid Learning at University Penentuan Jumlah dan Jadwal Petugas Keamanan untuk Masa Pandemi dan Pembelajaran Hybrid di Universitas

Main Article Content

Rainisa Maini Heryanto
Kartika Suhada
Winda Halim
Grace Vania

Abstract

The recovery condition after the Covid-19 pandemic in various sectors from the economic sector, education, and even tourism requires many adjustments in various factors. A university is an educational institution that applies the concept of online learning in pandemic conditions. Restrictions on activities carried out on campus have an influence on the workload of security officers. In this study, the number of security officers was determined during the pandemic and hybrid learning so as to minimize the costs that must be incurred by the university. Determining the number of security officers needed uses a workload analysis based on interviews and considers several factors such as the target level of environmental security, the wide coverage of the security area, and the workload of the security officers themselves. After knowing the number of security officers needed, then scheduling based on work shifts uses the Tibrewala, Philippe, and Brown algorithm so that the workload is evenly distributed. The results obtained show that during the pandemic 35 security officers are needed with an estimated allocation of IDR 156,103,200 per month and get a savings of IDR 84,287,500,- per month (35.1%). For hybrid learning, it takes 51 personnel with an estimated allocation of IDR 224,303,200,- per month. This cost increases by IDR 68.200.000,- per month (43.7%) of pandemic conditions. This research is expected to provide consideration for universities as one of the steps to make savings and be useful during the pandemic and hybrid learning.

Article Details

How to Cite
Heryanto, R. M., Suhada, K., Halim, W., & Vania, G. (2023). Determination the Number and Schedule of Security Officers for Pandemic Period and Hybrid Learning at University. PROZIMA (Productivity, Optimization and Manufacturing System Engineering), 7(1), 73-83. https://doi.org/10.21070/prozima.v7i1.1618
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Articles

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