Main Article Content

Abstract

The construction sector is one of the strongest sectors in supporting economic growth in Indonesia. In supporting the development and demands for the provision of services from the construction sector, as a state of law, the state of Indonesia has laws in the construction sector, one of which is on Occupational Health and Safety (K3). One of the efforts to minimize the consequences caused by work accidents, every worker is required to use Personal Protective Equipment (PPE). Lack of self-awareness and discipline of workers in the use of Personal Protective Equipment (PPE), can result in a fairly large risk of work accidents. So there needs to be automatic detection for workers in the use of good and correct PPE. This research is using the latest method from R-CNN, namely Mask Region Convolutional Neural Network (Mask R-CNN). The best model obtained is the epoch 35 parameter with a loss value of 0.1985 and a val_loss value of 0.1933 in 461s 922ms/step. Trial ith 250 images which produces an accuracy 0f 95%.

Keywords

Alat Pelindung Diri (APD) Keselamatan dan Kesehatan Kerja Mask R-CNN Personal Protective Equiment (PPE) Occupational Safety and Health Mask R-CNN

Article Details

Author Biographies

Milzamah Elvi Laily, Universitas Nurul Jadid

Universitas Nurul Jadid

Fathorazi Nur Fajri, Universitas Nurul Jadid

Sistem Informasi,Universitas Nurul Jadid

Gulpi Qorik Oktagalu Pratamasunu, Universitas Nurul Jadid

Teknik Informatika, Universitas Nurul Jadid
How to Cite
Laily, M. E., Fajri, F. N., & Pratamasunu, G. Q. O. (2022). Detection of the Use of Personal Protective Equipment (PPE) for Occupational Health and Safety Using the Mask Region Convolutional Neural Network (Mask R-CNN) Method. Jurnal Komputer Terapan , 8(2), 279–288. https://doi.org/10.35143/jkt.v8i2.5732

References

  1. M. N. R. Isya, "Rancang Bangun Sistem Peringatan Identifikasi Alat Pelindung Diri (APD) Menggunakan Metode You Only Look Once v4 (YOLOv4)," in Jurnal Conference on Automation Engineering and Its Application, 2021.
  2. J. M. Tumiwa, J. Tjakra and R. L. Inkiriwang, "Pengaruh Penerapan Alat Pelindung Diri Terhadap Produktivitas Tenaga Kerja Konstruksi Gedung Bertingkat Pembangunan Gedung Pendidikan FPIK Universitas Sam Ratulangi," Jurnal Sipil Statik, vol. 07, no. 09, 2019.
  3. J. Munawwaroh, F. N. Fajri and G. Q. O. Pratamasunu, "Personal Protective Equipment (PPE) Detection For Industrial Monitoring (Occupational Safety And Health) Using The You Only Look Once (Yolo) Method," Bulletin of Electrical Engineering and Informatics, vol. 99, no. 1, 2021.
  4. R. Mafra, R. Riduan and Z. Zulfikri, "Analisis Kepatuhan Penggunaan Alat Pelindung Diri (APD) Pada Peserta Keterampilan Tukang dan Pekerja Konstruksi," Jurnal Arsir, vol. 5, no. 1, pp. 48-63, 2021.
  5. M. Ulum, M. Zakariya, A. Fiqhi and H. Haryanto, "Rancang Sistem Pendeteksi Alat Pelindung Diri (APD) Berbasis Image Processing. Jurnal Ilmiah Teknik Informatika," Jurnal Ilmiah Teknik Informatika, Elektronika, dan Kontrol, vol. 01, no. 01, pp. 23-30, 2021.
  6. V. S. K. Delhi, R. Sankarlal and A. Thomas, "Detection of Personal Protective Equipment (PPE) Compliance on Construction Site Using Computer Vision Based Deep Learning Techniques," Frontiers in Built Environ, vol. 6, no. 136, 2020.
  7. G. Zhu, Z. Piao and S. C. Kim, "Tooth Detection and Segmentation with Mask R-CNN," International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 070-072, 2020.
  8. L. Cai, T. Long, Y. Dai and Y. Huang, "Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis," IEEE Access, vol. 8, pp. 44400-44409, 2020.
  9. Z. Yang, Y. Yuan, M. Zhang, X. Zhao, Y. Zhang and B. Tian, "Safety Distance Identification for Crane Drivers Based on Mask R-CNN," Sensors, vol. 19, no. 12, p. 2789, 2019.
  10. R. M. Mailoa and L. W. Santoso, "Deteksi Rompi dan Helm Keselamatan Menggunakan Metode YOLO dan CNN," Jurnal Infra, vol. 10, no. 2, pp. 56-62, 2022.
  11. P. K. Sari, G. . Q. O. Pratamasunu and F. . N. Fajri, "Deteksi Tangan Otomatis Pada Video Percakapan Bahasa Isyarat Indonesia Menggunakan Metode Deep Gated Recurrent Unit (GRU)," Jurnal Komputer Terapan, vol. 8, no. 1, p. 186–193, 2022.
  12. S. Ahlawat, A. Choudhary, A. Nayyar, S. Singh and B. Yoon, "Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)," Sensors, vol. 20, no. 12, p. 3344, 2020..
  13. V. M. P. Salawazo, D. P. J. Gea, R. F. Gea and F. Azmi, "Implementasi Metode Convolutional Neural Network (CNN) Pada Pengenalan Objek Video CCTV," Jurnal Mantik Penusa, vol. 03, no. 1.1, 2019.
  14. A. Wicaksono, M. H. Purnomo and E. M. Yuniarno, "Deteksi Pejalan Kaki Pada Zebra Cross Untuk Peringatan Dini Pengendara Mobil Menggunakan Mask R-CNN," Jurnal Teknik ITS, vol. 10, no. 02, 2021.
  15. T. Shibata, A. Teramoto, H. Yamada, N. Ohmiya, K. Saito and H. Fujita, "Automated detection and segmentation of early gastric cancer from endoscopic images using mask R-CNN," Applied Sciences, vol. 10, no. 11, p. 3842, 2020.
  16. F. N. Fajri, K. Malik and G. Q. O. Pratamasunu, "Metode Pengumpulan Data Pada Deteksi Pakaian Hijab Syar'I Berdasarkan Citra Digital Menggunakan Teachable machine Learning," Justek: Jurnal Sains dan Teknologi, vol. 5, no. 2, pp. 194-203, 2022.