Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Mobile and Forensics
Langi, Nelson Mandela Rande
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Neuroscience

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Embedded System for Automatic Mask Detection using YOLOv4 Deep Learning and PyQt5 Interface Fadllullah, Arif; Langi, Nelson Mandela Rande; Maulana, Ikhsan; Meilindy, Laura Nur; Akbar, Muhammad Adhiya Yudhistira; Rahman, Mukti Dika
Mobile and Forensics Vol. 7 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i1.11951

Abstract

The use of masks remains crucial, especially in high-risk areas for disease transmission, such as airports, schools, hospitals, and crowded places. However, some individuals continue to neglect wearing masks in these locations, leaving the area vulnerable to disease spread, including COVID-19. Therefore, this study proposes the development of an embedded system based on Raspberry Pi 4 for automatic mask detection using YOLOv4 deep learning and a PyQt5 interface. The system is designed to be simple and compact, featuring a user-friendly GUI to effectively detect mask usage on multiple faces in a single detection. Experimental results on 40 samples captured in real-time, with 4 samples taken per image capture and various mask colors and three mask-wearing angles, demonstrated that the average precision, recall, and F1_score rates were each 100%. This outcome proves that the proposed embedded system successfully detects masks on multiple faces with different colors and angles in a single detection with excellent accuracy.
Sistem Deep-Learning Yolov8 untuk Deteksi Penggunaan APD Secara Real-Time Langi, Nelson Mandela Rande; Fadllullah, Arif
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 1 (2026): January 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5051

Abstract

Although workplace safety regulations in construction are clear, many workers are still reluctant to use Personal Protective Equipment (PPE) due to a lack of awareness, work pressure, and limited facilities. As a result, the risk of serious accidents increases. Conventional approaches such as verbal warnings or CCTV monitoring are considered less effective for early detection and prevention of violations. This study proposes an automatic detection system for PPE usage in construction areas using YOLOv8. The model was trained on a secondary dataset of 3,569 images for 100 epochs, with a 60% training, 20% validation, and 20% test split. Testing on 90 real-time frames showed good performance in detecting 8 PPE classes, with an average precision of 0.935, recall of 0.806, and F1-measure of 0.862. The results indicate that the system can classify PPE usage with high accuracy. However, a recall below 1 suggests that some objects, particularly "not wearing glasses" and "not wearing shoes," failed to be detected. The F1-measure of 0.862 reflects a good balance between precision and recall.
Co-Authors Akbar, Muhammad Adhiya Yudhistira Arif Fadllullah Maulana, Ikhsan Meilindy, Laura Nur Rahman, Mukti Dika