The Role of Faster R-CNN Algorithm in the Internet of Things to Detect Mask Wearing: The Endemic Preparations

• Autorzy: Nasution Marah Doly , Al-Khowarizmi , Rahmat Romi Fadillah , Lubis Arif Ridho , Lubis Muharman

Identyfikatory

DOI

10.24425/ijet.2023.147689

• Języki publikacji: EN

Abstrakty

EN

Faster R-CNN is an algorithm development that continuously starts from CNN then R-CNN and Faster R-CNN. The development of the algorithm is needed to test whether the heuristic algorithm has optimal provisions. Broadly speaking, faster R-CNN is included in algorithms that are able to solve neural network and machine learning problems to detect a moving object. One of the moving objects in the current phenomenon is the use of masks. Where various countries in the world have issued endemic orations after the Covid 19 pandemic occurred. Detection tool has been prepared that has been tested at the mandatory mask door, namely for mask users. In this paper, the role of the Faster R-CNN algorithm has been carried out to detect masks poured on Internet of Thinks (IoT) devices to automatically open doors for standard mask users. From the results received that testing on the detection of moving mask objects when used reaches 100% optimal at a distance of 0.5 to 1 meter and 95% at a distance of 1.5 to 2 meters so that the process of sending detection signals to IoT devices can be carried out at a distance of 1 meter at the position mask users to automatic doors.

Słowa kluczowe

EN

Faster R-CNN; IoT; mask; endemic

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2023
• Tom: Vol. 69, No. 4
• Strony: 691--696
• Opis fizyczny: Bibliogr. 27 poz., fot., tab., wykr.

Twórcy

autor

Nasution Marah Doly

• Department of Mathematics Education,Universitas Muhammadiyah Sumatera Utara, Indonesia

autor

Al-Khowarizmi

• Department of Information Technology, UniversitasMuhammadiyah Sumatera Utara, Indonesia

autor

Rahmat Romi Fadillah

• Department of Information Technology,Universitas Sumatera Utara, Indonesia

autor

Lubis Arif Ridho

• Department of Management Informatics, PoliteknikNegeri Medan, Indonesia

autor

Lubis Muharman

• Department of Information Systems, TelkomUniversity, Indonesia

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).