SISTEM PEMANTAU PENGGUNAAN ALAT PELINDUNG DIRI (APD)PEKERJA SECARA REAL TIME
DOI:
https://doi.org/10.31000/jika.v9i3.14400Abstrak
Penggunaan Alat Pelindung Diri (APD) merupakan salah satu aspek penting untuk menjaga keselamatan pekerja di lingkungan kerja. Namun, masih banyak pekerja yang mungkin lupa atau sengaja tidak menggunakan kelengkapan APD ini dengan beberapa faktor, seperti, kepala terasa berat dan ketat dengan penggunaan helm, atau merasa panas ketika menggunakan rompi pelindung. Hal ini tentunya dapat menimbulkan risiko bahaya ketika sedang bekerja. Oleh karena itu, penelitian ini bertujuan untuk merancang sebuah sistem yang dapat memantau penggunaan APD pada pekerja. Sistem ini menerapkan algoritma YOLOv5 dalam proses pendeteksian kelengkapan APD pekerja tersebut. Dengan menggunakan bobot model yang dihasilkan pada proses training dengan Image Size 640, Epoch 300, Batch size 8, maka pengujian pendeteksian APD pada tangkapan kamera pengintai CCTV perusahaan, menghasilkan nilai akurasi tertinggi 81% (person/pekerja), 87% (helm), 78% (rompi pelindung), 71% (tanpa rompi) dan 77% (tanpa helm). Berdasarkan nilai akurasi yang dihasilkan, maka dapat disimpulkan bahwa algoritma YOLOv5 ini mampu mendeteksi objek apapun dengan baik. Sistem yang dihasilkan dapat membantu supervisor dalam memantau penggunaan APD oleh pekerja sehingga risiko bahaya dapat dihindari sedini mungkin.
Referensi
Afnur, N.A., Setiawan, R.A., Zahra, S. & Astharini, D. (2024). Deteksi Alat Pelindung Diri Secara Real-time Menggunakan Algoritma YOLO. JIK: Jurnal Ilmu Komputer, 9(2), 73~ 77. DOI: 10.47007/komp.v9i02.8993
Ahmed, M. I. B., Saraireh, L., Rahman, A., Al-Qarawi, S., Mhran, A., Al-Jalaoud, J., AlMudaifer, D., Al-Haidar, F., AlKhulaifi, D., Youldash, M. & Gollapalli, M. (2023). Personal Protective Equipment Detection: A Deep-Learning-Based Sustainable Approach. MDPI: Sustainability (Switzerland), 15(18):13990. DOI:10.3390/su151813990
Ashar, M. H., & Suarna, D. (2022). Implementasi Algoritma YOLOv5 dalam Mendeteksi Penggunaan Masker Pada Kantor Biro Umum Gubernur Sulawesi Barat. KLIK: Kajian Ilmiah Informatika dan Komputer, 3(3), 298 302. DOI: https://doi.org/10.30865/klik.v3i3.559.
Azzahri, L. M., Ikhwan, K. I. (2023). Hubungan Pengetahuan Tentang Penggunaan Alat Pelindung Diri (APD) dengan Kepatuhan Penggunaan APD pada Perawat di Puskesmas Kuok. Prepotif: Jurnal Kesehatan Masyarakat, 3, 50–57, https://doi.org/10.31004/prepotif.v3i1.442.
Deng, H., Ou, Z. & Deng, Y., (2021). Multi-angle fusion-based safety status analysis of construction workers. Int J Environ Res Public Health, 18. https://doi.org/10.3390/ijerph182211815
Djaohar, M., & Sunawar, A. (2022). Rancang Bangun Pengecekan Alat Pelindung Diri Menggunakan Algoritma You Only Look Once (YOLO). Journal of Electrical Vocational Education and Technology, 7(1), 12-18.
Jayanti, U., Ali, H., Reflis, R., Ramdhon, M., Utama, S., Adeko, R., Afirmansyah, A., Arifin, Z., Siswahyono, S. (2023). Analisis Penggunaan Alat Pelindung Diri dan Kecelakaan Kerja pada Pekerja Pabrik Kelapa Sawit di PT. Palma Mas Sejati Kabupaten Bengkulu Tengah. Jurnal Ilmu Keperawatan dan Kesehatan Masyarakat, 11(1), 272-278. DOI: https://doi.org/10.37676/jnph.v11i1.4138.
Mailoa, R. M., & Santoso, L. W. (2022). Deteksi Rompi dan Helm Keselamatan Menggunakan Metode YOLO dan CNN. Jurnal Infra, 10(2), 49-55.
Menteri Tenaga Kerja dan Transmigrasi (2010). Alat Pelindung Diri. Peraturan Menteri Tenaga Kerja dan Transmigrasi Republik Indonesia, No. PER.08/MEN/VII/2010.
Natalia, I. G., & Asmunin (2024). Deteksi Kelengkapan APD Keselamatan Pekerja Konstruksi Dengan Menggunakan Metode Convolutional Neural Network Dan Model Jaringan YOLOv5. Jurnal Manajemen Informatika, 16(1). https://ejournal.unesa.ac.id/index.php/jurnal-manajemen-informatika/article/view/56546
Nirvana, M.N. Rachmadi, R.F. & Purnama, I. K. E. (2023). Sistem Pendeteksi Alat Pelindung Diri (ADP) pada Pekerja Konstruksi Berbasis Convolutional Neural Network. Jurnal Teknik ITS, 12(3). ISSN: 2337-3539 (2301-9271 Print).
Rahma, L., Syaputra, H., Mirza, A.H. & Purnamasari, S.D. (2021). Objek Deteksi Makanan Khas Palembang Menggunakan Algoritma YOLO (You Only Look Once). J. Nas. Ilmu Komput., 2(3), 213–232.
Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection. Retrieved July 4, 2025, from: http://pjreddie.com/yolo/.
Taufiqurrochman, M. A. & Februariyanti, H. (2024). Rancang Bangun Aplikasi Deteksi Alat Pelindung Diri (APD) untuk Pekerja Proyek dengan Menggunakan Algoritma Yolov5. JTIK: Jurnal Teknologi Informasi dan Komunikasi), 8(2), DOI: https://doi.org/10.35870/jti k.v8i2.1960.
Wong, SS. & Soo, AL. (2019). Factors Influencing Safety Performance in the Constraction Industri. Journal of Social Science and Humanities, 16(3), 1-9. ISSN: 1823-884x
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