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All Journal Journal of Electrical Engineering and Informatics Journal of Architecture, Research and Innovation
Harun Musa
Politeknik Negeri Ujung Pandang
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology

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Optimalisasi Pencahayaan Alami dan Buatan pada Laboratorium Komputer Menggunakan Simulasi Dialux Rahmayanti Rahmayanti; Nur Mutmainnah; Harun Musa
JARI: Journal of Architecture, Research and Innovation Vol. 2 No. 1 (2025): Desember
Publisher : Program Studi Teknik Arsitektur, Fakultas Teknik, Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/jari.v2i1.1719

Abstract

This research examines the optimization of natural and artificial lighting in the ABG Computer Laboratory using Dialux simulation. The study aims to evaluate daylight performance based on window configuration, investigate the causes of deviation between simulation results and on-site illuminance measurements, and develop an artificial lighting layout that fulfills visual comfort standards for computer-based learning spaces. The initial daylight simulation revealed that 59.8% of the total floor area achieved illumination levels of ≥300 lux; however, field measurements showed substantially lower illuminance due to shading from dense vegetation in front of the façade, leading to uneven light distribution across the room. The artificial lighting simulation using 50W 6500K LED luminaires successfully provided uniform illuminance of ≥500 lux throughout the working plane. These findings confirm that a hybrid lighting system—integrating optimized daylight and artificial lighting—is the most reliable and energy-efficient solution for computer-oriented educational environments.
Real-Time Safety Helmet and Vest Compliance Detection Using YOLOv8 with Web-Based Monitoring and Telegram Alerts Muhammad Rijal; Muh. Erdin; Harun Musa
Journal of Electrical Engineering and Informatics Vol. 4 No. 1 (2026): Journal of Electrical Engineering and Informatics
Publisher : Fakultas Teknik Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jeeni.v4i1.13712

Abstract

Workplace safety remains a critical concern in high-risk industrial sectors due to frequent non-compliance with Personal Protective Equipment (PPE) regulations. This study aims to develop and evaluate a real-time, web-based system for automatically detecting compliance with two essential PPE items, safety helmets and safety vests, among workers in industrial work environments, including its detection accuracy, generalization on unseen data, and notification reliability. This research adopts a Research and Development (R&D) approach that combines a machine-learning experimentation pipeline for model development with a software prototyping process for the monitoring application, encompassing dataset collection from Roboflow (2,777 images across six classes: Hardhat, No Hardhat, Safety Vest, No Safety Vest, Person, and Background), data preprocessing, training of a YOLOv8n model (100 epochs, image size 800×800, batch size 16, SGD optimizer), performance evaluation on the validation set, qualitative functional testing, and implementation into a Flask-based website prototype integrated with Telegram Bot API notifications. On the validation set, the trained YOLOv8n model achieved a mean Average Precision at IoU 0.5 (mAP50) of 0.681 and an mAP50-95 of 0.416, at the best-fitness checkpoint (epoch 77 of 100). At the operational confidence threshold of 0.262 identified from the F1-Confidence curve, the model achieved an F1-Score of 0.69. Independent evaluation on the held-out 84-image test set is identified as a priority for follow-up work. Novelty: Unlike prior studies that generally stop at the model training and evaluation stage, and unlike [18], which implemented a YOLO-based PPE completeness dashboard without an automated alerting mechanism, this research integrates the YOLOv8 detection model directly into a real-time monitoring dashboard with an automatic Telegram text-and-image notification mechanism, enabling field supervisors to receive instant alerts whenever a PPE violation is detected. The resulting prototype demonstrates initial feasibility for supporting PPE compliance monitoring in industrial workplaces, pending more extensive field validation.
Co-Authors Erdin, Muh. Muhammad Rijal Nur Mutmainnah Rahmayanti Rahmayanti