Implementation of Deep Learning for Personal Protective Equipment (PPE) Detection on Workers Using the YOLO Algorithm
DOI:
https://doi.org/10.12928/mf.v7i2.13884Keywords:
Personal Protective Equipment, Object Detection, YOLO, Deep Learning, Oil IndustryAbstract
Occupational accidents represent a major challenge in the construction and manufacturing industries. This study aims to develop a deep learning model for real-time detection of personal protective equipment (PPE) usage using the YOLOv5 algorithm. Utilizing a dataset that includes four classes (hardhat, no hardhat, coverall, and no coverall), the model was trained and evaluated based on precision, recall, and mean Average Precision (mAP) metrics. The results demonstrated that the model achieved a high accuracy level with an mAP of 0.91 and stable performance. The model can also rapidly and effectively detect safety attributes even in complex work environments, such as varied lighting conditions and numerous background objects. Based on usability testing results of 85.35% and satisfactory black box testing, this study produced a prototype web-based application enabling efficient and effective PPE monitoring. The application is designed to support the improvement of workplace safety across various industrial sectors in a more practical and adaptive manner. It is expected to increase PPE compliance, reduce accident risks, and contribute significantly to workplace safety in the industry. The conclusion indicates that the YOLOv5 algorithm holds great potential for implementation in technology-based safety monitoring systems and supports the development of a safer and more modern industry.
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B. P. Nugroho, Y. Prihati, and S. T. Galih, “Implementasi Algoritma Yolo V5 Dalam Rancangan Aplikasi Pendeteksi Plat Nomor Kendaraan,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 7, no. 3, Art. no. 3, May 2024, doi: 10.31539/intecoms.v7i3.10376.
L. Putriwardani and S. Susilawati, “Pengembangan Teknologi Digital Terhadap Pemenuhan Keselamatan Konstruksi dI Indonesia,” Alahyan J. Pengabdi. Masy. Multidisiplin, vol. 2, no. 1, Art. no. 1, May 2024, doi: 10.61492/ecos-preneurs.v2i1.93.
J. R. Yasiri, R. Prathivi, and Susanto, “Detection of Plastic Bottle Waste Using YOLO Version 5 Algorithm,” Sink. J. Dan Penelit. Tek. Inform., vol. 9, no. 1, Art. no. 1, Jan. 2025, doi: 10.33395/sinkron.v9i1.14242.
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L. Liu et al., “Multi-Task Intelligent Monitoring of Construction Safety Based on Computer Vision,” Buildings, vol. 14, no. 8, p. 2429, Aug. 2024, doi: 10.3390/buildings14082429.
C. Li, J. Wang, B. Luo, T. Yin, B. Liu, and J. Lu, “SD-YOLOv5: a rapid detection method for personal protective equipment on construction sites,” Front. Built Environ., vol. 11, Apr. 2025, doi: 10.3389/fbuil.2025.1563483.
Jeicman Samperante, I Made Agus Wirahadi Putra, and Putu Adi Guna Permana, “Implementasi Arsitektur Yolo V8 Dalam Mendeteksi Alat Pelindung Diri (APD) Di Sektor Konstruksi Dan Industri,” Semin. Has. Penelit. Inform. Dan Komput. SPINTER Inst. Teknol. Dan Bisnis STIKOM Bali, vol. 2, no. 1, pp. 661–666, Mar. 2025.
R. Pusparina A and R. Rahmadewi, “Deteksi Objek Berbasis Yolov8 Untuk Mendukung Keselamatan Kerja Di Lokasi Konstruksi,” JATI J. Mhs. Tek. Inform., vol. 9, no. 2, pp. 3188–3195, Apr. 2025, doi: 10.36040/jati.v9i2.13257.
R. Ritnawati et al., Kesehatan dan Keselamatan Kerja dalam Dunia Perusahaan. 2025. Accessed: July 20, 2025. [Online]. Available: https://drive.google.com/file/d/1W3eAp0ULrhDmh2xKLg9TmYmyz12N0a-p/view?usp=sharing
M. Khatami Fahmi Putra, L. M Zainul, K. Rusba, Y. Nawawi, and H. Hardiyono, “Inovasi K3: Integrasi AI dan IoT untuk Meningkatkan Keselamatan Kerja,” Ranah Res. J. Multidiscip. Res. Dev., vol. 6, no. 5, pp. 2231–2239, Aug. 2024, doi: 10.38035/rrj.v6i5.1056.
A. Surachman, B. Kusumo, A. Sulistyohati, A. Wibowo, M. Yusuf, and A. S. E. Nugroho, Komputer dan Masyarakat. Banyumas: Ganesha Kreasi Semesta, 2024.
N. R. Diasri, A. W. Baeti, and A. Prabowo, “Pengaruh Penerapan Algoritma Pemrograman Dalam Dunia Pekerjaan (Studi Kasus: Metode Deep Learning),” J. CoSciTech Comput. Sci. Inf. Technol., vol. 6, no. 1, Art. no. 1, May 2025, doi: 10.37859/coscitech.v6i1.8531.
U. Zaelani, S. Fazriyah, E. Aisyah, N. M. Cahyati, and A. Gunawan, “Studi Literatur : Pentingnya Implementasi Sistem Manajemen Kesehatan Dan Keselamatan Kerja Di Perusahaan Manufaktur,” J. Perubahan Ekon., vol. 8, no. 12, 2024, [Online]. Available: https://jurnalhost.com/index.php/jpe/article/view/2190
L. Susanti, N. K. Daulay, and B. Intan, “Sistem Absensi Mahasiswa Berbasis Pengenalan Wajah Menggunakan Algoritma YOLOv5,” JURIKOM J. Ris. Komput., vol. 10, no. 2, Art. no. 2, Apr. 2023, doi: 10.30865/jurikom.v10i2.6032.
A. Gapur, D. Wahiddin, T. A. Mudzakir, and J. Indra, “Personal Protctive Equipment Detection for Occupational Safety and Health Using Yolov8 in Manufacturing Companies,” J. Tek. Inform. Jutif, vol. 5, no. 4, Art. no. 4, Aug. 2024, doi: 10.52436/1.jutif.2024.5.5.2619.
L. Dewi Putrie, S. Madenda, L. Octavia, F. Nurwidya, and D. T. Susetianingtias, Komputer Vision, Kecerdasan Artifisial, dan Sistem Tertanam Hasil Penelitian Terapan. Depok: Penerbit Gunadarma.
A. Z. D. N. Adiya, D. L. Anggraeni, and I. Albana, “Analisa Perbandingan Penggunaan Metodologi Pengembangan Perangkat Lunak (Waterfall, Prototype, Iterative, Spiral, Rapid Application Development (RAD)),” Merkurius J. Ris. Sist. Inf. Dan Tek. Inform., vol. 2, no. 4, pp. 122–134, June 2024, doi: 10.61132/merkurius.v2i4.148.
A. Fu’adi and A. Prianggono, “Analisa dan Perancangan Sistem Informasi Akademik Akademi Komunitas Negeri Pacitan Menggunakan Diagram UML dan EER,” J. Ilm. Teknol. Inf. Asia, vol. 16, no. 1, Art. no. 1, Jan. 2022, doi: 10.32815/jitika.v16i1.650.
M. Z. Fahri, “Sistem Deteksi Objek Manusia Menggunakan Algoritma Yolov8 Berbasis Kamera Depth Sensor (Studi Kasus: Cv. Ateri Global Teknologi),” skripsi, Universitas Sangga Buana YPKP, 2024. Accessed: July 20, 2025. [Online]. Available: https://repository.usbypkp.ac.id/3720/
M. Nasir et al., “Analisis Decision Table Testing untuk Pengujian Blackbox Website Pusat Studi Bencana IPB | JATISI (Jurnal Teknik Informatika dan Sistem Informasi),” Dec. 2024, Accessed: July 20, 2025. [Online]. Available: https://jurnal.mdp.ac.id/index.php/jatisi/article/view/9618
Z. Mutaqin Subekti, K. Mukiman, Subandri, M. Lutfi Sulthon Auliya Sulistyono, and R. Eka Putra, “Rancang Bangun Infrastruktur Web Server Berbasis Docker Pada Ubuntu Server,” J. Teknol. Inf. Dan Digit., vol. 2, no. 1, pp. 144–151, Dec. 2024.
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