Implementasi Algoritma YOLOv5 dalam Mendeteksi Penggunaan Masker Pada Kantor Biro Umum Gubernur Sulawesi Barat
DOI:
https://doi.org/10.30865/klik.v3i3.559Keywords:
YOLOv5; Masks; Covid19; Office; West SulawesiAbstract
The implementation of health protocols, especially the uses of masks, is an obligation that must be carried out by the people in Indonesia at that time in preventing the Covid-19 Pandemic. However, in practice it is still common to find various non-compliances by the community in carrying out their obligation to wear masks when outside the home. This study aims to see how the performance of the YOLOv5 algorithm in detecting the use of masks in the community specifically at the West Sulawesi Province is an area that is always visited by many regional employees of the Province of West Sulawesi. The results obtained are that the YOLOv5 algorithm can detect multiple images of one person or multiple people in one image. It can be seen from the implementation results in the West Sulawesi governor's office environment, it can be seen from the video that the results of the prediction box are correct in detecting the image of a mask on the face
Downloads
References
WHO, “Materi Komunikasi Risiko COVID-19 untuk Fasilitas Pelayanan Kesehatan,” World Heal. Organ., pp. 1–11, 2020, [Online]. Available: https://www.who.int/docs/default-source/searo/indonesia/covid19/risk-communication-for-healthcare-facility.pdf?sfvrsn=9207787a_2.
N. Kadek, S. Sanistya, F. Hukum, and U. Udayana, “Masyarakat Kota Denpasar Dalam Pengenaan Masker??,” Vol., vol. 10, no. 10, pp. 984–993, 2022.
S. A. Sanjaya and S. A. Rakhmawan, “How Can Machine Learning Help the Authorities?,” Semin. Nas. Off. Stat., vol. 2020, no. 1, pp. 370–376, 2021, doi: 10.34123/semnasoffstat.v2020i1.497.
M. Harahap, M. Suryani, and C. E. Situmeang, “Identification of Face Mask with YOLOv4 Based on Outdoor Video,” vol. 6, no. 1, pp. 127–134, 2021.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.
J. Nelson and J. Solawetz, “YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS,” roboflow, 2020. https://blog.roboflow.com/yolov5-is-here/.
J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” 2018, [Online]. Available: http://arxiv.org/abs/1804.02767.
J. Solawetz, “YOLOv5 New Version - Improvements And Evaluation,” roboflow, 2020. https://blog.roboflow.com/yolov5-improvements-and-evaluation/.
A. R. Wasril, M. S. Ghozali, and M. B. Mustafa, “Pembuatan Pendeteksi Obyek Dengan Metode You Only Look Once (Yolo) Untuk Automated Teller Machine (Atm),” Maj. Ilm. UNIKOM, vol. 17, no. 1, pp. 69–76, 2019, doi: 10.34010/miu.v17i1.2240.
R. Lubis, “Machine Learning ( Convolutional Neural Networks ) for Face Mask Detection in Image and Video,” pp. 1–20, 2020.
D. Hussain, M. Ismail, I. Hussain, R. Alroobaea, S. Hussain, and S. S. Ullah, “Face Mask Detection Using Deep Convolutional Neural Network and MobileNetV2-Based Transfer Learning,” Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/1536318.
J. Pradipta and A. M. Nazaruddin, Antipanik! Buku Panduan Virus Corona. Elex Media Komputindo, 2020.
D. Annisa, “Situasi Terkini Perkembangan Coronavirus Disease (COVID-19) 18 Juli 2021,” Situasi Infeksi Emerging, 2021. https://covid19.kemkes.go.id/situasi-infeksi-emerging/situasi-terkini-perkembangan-coronavirus-disease-covid-19-18-juli-2021.
S. N. Aidah, KITAB Sejarah Covid-19. Indoensia: PENERBIT KBM INDONESIA, 2021.
C. Münker and O. Stenroos, “Object detection from images using convolutional neural networks,” Thesis, pp. 1–87, 2017, [Online]. Available: https://www.ke.tu-darmstadt.de/lehre/arbeiten/master/2016/Muenker_Christoph.pdf.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Algoritma YOLOv5 dalam Mendeteksi Penggunaan Masker Pada Kantor Biro Umum Gubernur Sulawesi Barat
ARTICLE HISTORY
Issue
Section
Copyright (c) 2022 Muhammad Harun Ashar, Dedi Suarna
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
