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All Journal Jurnal Komputer, Informasi Teknologi, dan Elektro (KITEKTRO)
Roslidar Roslidar
Departemen Teknik Elektro dan Komputer, Universitas Syiah Kuala

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Real-Time PPE Detection for Utility Field Workers Using YOLOv11 on Raspberry Pi with Automated Safety Reporting Israk Faradila; Roslidar Roslidar; Fathurrahman Fathurrahman; Yudha Nurdin; Mohd Syaryadhi
Jurnal Komputer Informasi Teknologi dan Elektro Vol. 11 No. 1 (2026): April
Publisher : Departemen Teknik Elektro dan Komputer Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/kitektro.v11i1.1506

Abstract

The level of compliance with the use of Personal Protective Equipment (PPE) in the work environment, especially in the field, remains a serious challenge with a direct impact on worker safety. Manual supervision is considered ineffective because it is subjective and limited in scope, time, and space. This research aims to develop an automatic PPE detection system using deep learning that can recognize five main objects: helmets, vests, gloves, shoes, and people. The system was designed to automatically detect real-time PPE availability and record workers' occupational safety status in the field. This research used the Convolutional Neural Network (CNN) with the YOLOv11 nano variant architecture, due to its advantages in efficiency and inference speed. The dataset comprised 1,471 images collected from PT PLN Aceh field documentation and the Roboflow Universe platform, which were expanded to 7,310 images following data augmentation. The dataset was split into training (96%), validation (2%), and testing (2%) subsets. The model was trained for 150 epochs and deployed on a Raspberry Pi 4 B for real-time inference. Evaluation results show a mean Average Precision at IoU 0.5 (mAP@0.5) of 90%, precision of 91.8%, and recall of 82%. The deployed system operates at 5–8 frames per second (FPS) and automatically logs worker safety status to Excel reports, demonstrating its practicality for real-time occupational safety monitoring.
Sistem Pemantauan Kualitas Air Sungai Secara Real Time Berbasis Internet of Things Fadlurrahman Al Hafizh Redi; Roslidar Roslidar; Mohd Syaryadhi; Alfatirta Mufti; Alfisyahrin Alfisyahrin; Zulhelmi Zulhelmi
Jurnal Komputer Informasi Teknologi dan Elektro Vol. 11 No. 1 (2026): April
Publisher : Departemen Teknik Elektro dan Komputer Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/kitektro.v11i1.1653

Abstract

Pencemaran air sungai akibat aktivitas manusia berdampak negatif terhadap lingkungan dan kesehatan masyarakat. Pemantauan kualitas air secara manual dinilai kurang efektif karena tidak mampu menyediakan data secara cepat dan berkelanjutan. Penelitian ini bertujuan untuk merancang sistem pemantauan kualitas air sungai secara real-time berbasis Internet of Things (IoT). Sistem ini dikembangkan menggunakan NodeMCU ESP8266 yang terintegrasi dengan sensor pH, dissolved oxygen (DO), kekeruhan, suhu, dan debit aliran. Data sensor diambil setiap 30 menit dan ditampilkan melalui aplikasi seluler. Hasil pengujian menunjukkan bahwa sistem mampu melakukan pemantauan secara otomatis serta mengklasifikasikan kondisi air menjadi tiga kategori: clean (bersih), polluted (tercemar), dan offline. Selain itu, sistem ini dilengkapi dengan alarm sebagai mekanisme peringatan dini. Dengan demikian, sistem yang dikembangkan dapat mendukung upaya pemantauan kualitas air secara efektif dan berkelanjutan.
Co-Authors Alfatirta Mufti Alfisyahrin Alfisyahrin Fadlurrahman Al Hafizh Redi Fathurrahman Fathurrahman Israk Faradila Mohd Syaryadhi Yudha Nurdin Zulhelmi .