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All Journal Jurnal Algoritma Jurnal Informatika dan Teknik Elektro Terapan PROCEEDING IC-ITECHS 2014 SMARTICS Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) KOMPUTIKA - Jurnal Sistem Komputer JIKA (Jurnal Informatika) Infotech: Journal of Technology Information SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Algoritma Proceeding of International Conference Health, Science And Technology (ICOHETECH) Journal of Software Engineering and Information System (SEIS) SmartComp
Pradana, Afu Ichsan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Personal Protective Equipment Completeness Monitoring System Using YOLO-Based Computer Vision Akmal, Baasith Khoiruddin; Lestari, Wiji; Pradana, Afu Ichsan
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10172

Abstract

Workplace safety in the construction sector remains a critical concern, primarily due to low compliance with Personal Protective Equipment (PPE) standards. To address this, this study develops and evaluates a real-time PPE monitoring system, conducting a comparative analysis of two state-of-the-art object detection models: YOLOv8s and YOLOv11s. The system is designed to detect three essential PPE items: helmets, masks, and vests, and both models were trained on a custom dataset of 9,202 augmented images over 200 epochs. The final evaluation on an unseen test set revealed highly competitive performance. While YOLOv8s achieved a marginally higher mAP@0.5 (90.8%), YOLOv11s demonstrated superior precision (92.0%) and better performance on the stricter mAP@0.5:0.95 metric (54.4%). Based on this nuanced trade-off and its significantly higher computational efficiency (15% fewer parameters), YOLOv11s was selected as the optimal model. The chosen model achieved a real-time inference speed of approximately 112 FPS. A functional web-based prototype was developed using Flask to demonstrate the system's practical application. These findings confirm that YOLOv11s offers a more balanced and efficient solution for automating PPE compliance monitoring and highlight that a holistic evaluation beyond a single metric is crucial for deploying robust computer vision systems in real-world safety applications.
Co-Authors Aji, Sindhu Purnomo Akmal, Baasith Khoiruddin arsanto, arsanto Ashidiqi, Andhyka Yoga Atina, Vihi Azizah, Shelvi Dwi Hartanti Harjanto, Prabandaru Pramu Harsanto, Harsanto Insaan, Nashiirotul Junantoro, Hagai Kusuma, Adri Surya Maulindar, Joni Nugroho, Nasrun Nurchim Nurchim Pamekas, Bondan Wahyu Permana, Aditya Candra Permatasari, Hanifah Pipin Widyaningsih Pratama, Fandi Aziz Putra, Enggar Wijaya Putra, Wihan Perkasa Nugraha Rakadipa, Dhimas Arya Rengga, Nikodemos Rifky, Raja Saputra, Ilham Tristadika Setiawan, Gilang Sopingi Sopingi, Sopingi Tory, Alfa Rado Andre Yusa Saka Wahyudi Wahyudi Wijanarko, Restu Gilang Wiji Lestari Wijiyanto Wijiyanto, Wijiyanto Yusuf Maulana, Yusuf