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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH
Raihana Aqila
Politeknik Negeri Bandung, West Bandung, West Java, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Comparison of Response Time of Artificial Intelligence Mask Detection Program Implemented on Several Computer and Microcontroller Platforms Eril Mozef; Raihana Aqila
IJISTECH (International Journal of Information System and Technology) Vol 6, No 4 (2022): Decembar
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i4.264

Abstract

The use of masks during the Covid-19 pandemic is crucial to prevent transmission of the Covid-19 virus. Everyone is required to wear a mask in public places both indoors and outdoors. Therefore, a mask detection device is urgently needed to allow visitors to enter public facilities and reduce interaction between officers and visitors. Currently, many algorithms and programs for detecting masked faces based on artificial intelligence have been proposed using cameras and image processing. However, unfortunately, the implementation, realization, and application in public places are still ineffective. Beside the reability is still not high enough, another factor is the response time which needs to be considered when implemented on a computer or a microcontroller device. In this regard, this research focused on measuring the response time of an artificial intelligence-based mask detection program using a camera and image processing and its implementation on several computer and microcontroller platform. The response time of several computer and microcontroller devices is needed to provide an overview of the relationship between system execution speed and costs. When we use a high speed computer, the response time is fast, but the cost of the device is high. In contrast, by using a microcontroller-type device means the costs are low but the response time is slow, which will eventually cause problems in the visitor queuing system. In this research, the Balaji Srinivasan mask detection program was successfully implemented on several computer and microcontroller devices. The results are as follows: PC core i3-5005 2 GHz, 4 GB RAM: 0.456 seconds, PC core i5-3207 RAM 4 GB, 3.2 GHz SSD 128 GB: 0.382 seconds, PC core i5-8250 RAM 8 GB: 0.393 seconds, AMD Ryzen 5 3550H 2.1 GHz RAM 8 GB SSD 512GB: 0.303 seconds, Raspberry Pi 3 B+, 1.4 GHz RAM 1 GB: 12.03 seconds, Raspberry Pi 4 1.6 GHz RAM 4 GB: 0.89 seconds. So that the shortest average delay respon time for the PC type is AMD Ryzen 5 3550H 2.1 GHz RAM 8 GB SSD 512 GB of 0.303 seconds, and for the microcontroller type is Raspberry Pi 4 of 0.89 seconds.
Comparison of Response Time of Artificial Intelligence Mask Detection Program Implemented on Several Computer and Microcontroller Platforms Eril Mozef; Raihana Aqila
IJISTECH (International Journal of Information System and Technology) Vol 6, No 4 (2022): Decembar
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i4.264

Abstract

The use of masks during the Covid-19 pandemic is crucial to prevent transmission of the Covid-19 virus. Everyone is required to wear a mask in public places both indoors and outdoors. Therefore, a mask detection device is urgently needed to allow visitors to enter public facilities and reduce interaction between officers and visitors. Currently, many algorithms and programs for detecting masked faces based on artificial intelligence have been proposed using cameras and image processing. However, unfortunately, the implementation, realization, and application in public places are still ineffective. Beside the reability is still not high enough, another factor is the response time which needs to be considered when implemented on a computer or a microcontroller device. In this regard, this research focused on measuring the response time of an artificial intelligence-based mask detection program using a camera and image processing and its implementation on several computer and microcontroller platform. The response time of several computer and microcontroller devices is needed to provide an overview of the relationship between system execution speed and costs. When we use a high speed computer, the response time is fast, but the cost of the device is high. In contrast, by using a microcontroller-type device means the costs are low but the response time is slow, which will eventually cause problems in the visitor queuing system. In this research, the Balaji Srinivasan mask detection program was successfully implemented on several computer and microcontroller devices. The results are as follows: PC core i3-5005 2 GHz, 4 GB RAM: 0.456 seconds, PC core i5-3207 RAM 4 GB, 3.2 GHz SSD 128 GB: 0.382 seconds, PC core i5-8250 RAM 8 GB: 0.393 seconds, AMD Ryzen 5 3550H 2.1 GHz RAM 8 GB SSD 512GB: 0.303 seconds, Raspberry Pi 3 B+, 1.4 GHz RAM 1 GB: 12.03 seconds, Raspberry Pi 4 1.6 GHz RAM 4 GB: 0.89 seconds. So that the shortest average delay respon time for the PC type is AMD Ryzen 5 3550H 2.1 GHz RAM 8 GB SSD 512 GB of 0.303 seconds, and for the microcontroller type is Raspberry Pi 4 of 0.89 seconds.
Co-Authors Eril Mozef Veithzal Rivai Zainal