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


  • Al-Khowarizmi Al-Khowarizmi Universitas Muhammadiyah Sumatera Utara
  • Marah Doly Nasution Universitas Muhammadiyah Sumatera Utara
  • Romi Fadillah Rahmat Universitas Muhammadiyah Sumatera Utara
  • Arif Ridho Lubis Politeknik Negeri Medan
  • Muharman Lubis Telkom University


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

Author Biography

Al-Khowarizmi Al-Khowarizmi, Universitas Muhammadiyah Sumatera Utara

Department of Information Technology


S. Singh, U. Ahuja, M. Kumar, K. Kumar, and M. Sachdeva, “Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment,” Multimed. Tools Appl., vol. 80, no. 13, pp. 19753–19768, 2021, doi:

W. Fang et al., “A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network,” Adv. Eng. Informatics, vol. 39, pp. 170–177, 2019, doi:

Z.-Q. Zhao, P. Zheng, S. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE Trans. neural networks Learn. Syst., vol. 30, no. 11, pp. 3212–3232, 2019, doi: 10.1109/TNNLS.2018.2876865.

C. Cao et al., “An improved faster R-CNN for small object detection,” Ieee Access, vol. 7, pp. 106838–106846, 2019, doi: 10.1109/ACCESS.2019.2932731.

S. Sulistyawati et al., “Knowledge, attitudes, practices and information needs during the covid-19 pandemic in indonesia,” Risk Manag. Healthc. Policy, vol. 14, p. 163, 2021, doi: 10.2147/RMHP.S288579.

F. Kahar, G. D. Dirawan, S. Samad, N. Qomariyah, and D. E. Purlinda, “The epidemiology of COVID-19, attitudes and behaviors of the community during the Covid pandemic in Indonesia,” structure, vol. 10, p. 8, 2020, doi: 10.38124/IJISRT20AUG670.

U. Anand et al., “Novel coronavirus disease 2019 (COVID-19) pandemic: from transmission to control with an interdisciplinary vision,” Environ. Res., vol. 197, p. 111126, 2021, doi: 10.1016/j.envres.2021.111126.

F. Nurahmadi, F. Lubis, and P. I. Nainggolan, “Analysis Of Deep Learning Architecture In Classifying SNI Masks,” J. INFORMATICS Telecommun. Eng., vol. 5, no. 2, pp. 473–482, 2022, doi: 10.31289/jite.v5i2.6341.

P. Forouzandeh, K. O’Dowd, and S. C. Pillai, “Face masks and respirators in the fight against the COVID-19 pandemic: An overview of the standards and testing methods,” Saf. Sci., vol. 133, p. 104995, 2021, doi: 10.1016/j.ssci.2020.104995.

Al-Khowarizmi and Suherman, “Classification of Skin Cancer Images by Applying Simple Evolving Connectionist System,” IAES Int. J. Artif. Intell., vol. 10, no. 2, pp. 421–429, 2021, doi: 10.11591/ijai.v10.i2.pp421-429.

J. Aaron and T.-L. Chew, “A guide to accurate reporting in digital image processing–can anyone reproduce your quantitative analysis?,” J. Cell Sci., vol. 134, no. 6, p. jcs254151, 2021, doi: 10.1242/jcs.254151.

R. Herrera-Pereda, A. T. Crispi, D. Babin, W. Philips, and M. H. Costa, “A Review On digital image processing techniques for in-Vivo confocal images of the cornea,” Med. Image Anal., vol. 73, p. 102188, 2021, doi:

R. Syah and A.-K. Al-Khowarizmi, “Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 7, no. 1, p. 154, 2021, doi: 10.26555/jiteki.v7i1.20508.

A. Khowarizmi, Akhm, M. Lubis, and A. R. Lubis, “Classification of Tajweed Al-Qur’an on Images Applied Varying Normalized Distance Formulas,” no. 3, pp. 21–25, 2020, doi: 10.1145/3396730.3396739.

A. R. Lubis, S. Prayudani, Y. Y. Lase, and Y. Fatmi, “Similarity Normalized Euclidean Distance on KNN Method to Classify Image of Skin Cancer,” in 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2021, pp. 68–73, doi: 10.1109/ISRITI54043.2021.9702826.

S. Suherman, F. Fahmi, Z. Herry, M. Al-Akaidi, and Al-Khowarizmi, “Sensor Based versus Server Based Image Detection Sensor using the 433 Mhz Radio Link,” in 2020 4rd International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), 2020, pp. 7–10, doi: 10.1109/ELTICOM50775.2020.9230502.

I. H. Sarker, “Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective,” SN Comput. Sci., vol. 2, no. 5, pp. 1–22, 2021, doi:

X. Mou, X. Chen, J. Guan, B. Chen, and Y. Dong, “Marine target detection based on improved faster R-CNN for navigation radar PPI images,” in 2019 International Conference on Control, Automation and Information Sciences (ICCAIS), 2019, pp. 1–5, doi: 10.1109/ICCAIS46528.2019.9074588.

V. Kafedziski, S. Pecov, and D. Tanevski, “Detection and classification of land mines from ground penetrating radar data using faster R-CNN,” in 2018 26th Telecommunications Forum (TELFOR), 2018, pp. 1–4, doi: 10.1109/TELFOR.2018.8612117.

B. Zhu, X. Wu, L. Yang, Y. Shen, and L. Wu, “Automatic detection of books based on Faster R-CNN,” in 2016 third international conference on digital information processing, data mining, and wireless communications (DIPDMWC), 2016, pp. 8–12, doi: 10.1109/DIPDMWC.2016.7529355.

R. Gavrilescu, C. Zet, C. Foșalău, M. Skoczylas, and D. Cotovanu, “Faster R-CNN: an approach to real-time object detection,” in 2018 International Conference and Exposition on Electrical And Power Engineering (EPE), 2018, pp. 165–168, doi: 10.1109/ICEPE.2018.8559776.

Y. Chen, H. Wang, W. Li, C. Sakaridis, D. Dai, and L. Van Gool, “Scale-aware domain adaptive faster r-cnn,” Int. J. Comput. Vis., vol. 129, no. 7, pp. 2223–2243, 2021, doi:

R. Meng, S. G. Rice, J. Wang, and X. Sun, “A fusion steganographic algorithm based on faster R-CNN,” Comput. Mater. Contin., vol. 55, no. 1, pp. 1–16, 2018, doi:

J. Julham, M. Lubis, A. R. Lubis, A.-K. Al-Khowarizmi, and I. Kamil, “Automatic face recording system based on quick response code using multicam,” IAES Int. J. Artif. Intell., vol. 11, no. 1, p. 327, 2022, doi:

M. Meyer and G. Kuschk, “Automotive radar dataset for deep learning based 3D object detection,” EuRAD 2019 - 2019 16th Eur. Radar Conf., no. January 2019, pp. 129–132, 2019.

Y. Liu, P. Sun, N. Wergeles, and Y. Shang, “A survey and performance evaluation of deep learning methods for small object detection,” Expert Syst. Appl., vol. 172, p. 114602, 2021, doi:

B. Benjdira, T. Khursheed, A. Koubaa, A. Ammar, and K. Ouni, “Car detection using unmanned aerial vehicles: Comparison between faster r-cnn and yolov3,” in 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), 2019, pp. 1–6, doi:






Applied Informatics