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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Pepadun
Ridho Sholehurrohman
Department of Computer Science, Universitas Lampung
Computer Science & IT Decision Sciences, Operations Research & Management

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Assessing Detection and Classification Performance for Vehicle License Plate Colors Using YOLOv5, YOLOv7, YOLOv8, and YOLOv9 Ridho Sholehurrohman; Muhaqiqin Muhaqiqin; Igit Sabda Ilman; Reza Habibi
Jurnal Pepadun Vol. 7 No. 1 (2026): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v7i1.349

Abstract

This study assesses the detection and classification performance of YOLOv5, YOLOv7, YOLOv8, and YOLOv9 for vehicle license plate colors in Indonesia, supporting the electronic ticketing system (e-tilang). The dataset consisted of 1,214 images from video footage captured in Bandar Lampung, comprising five color categories: black, white, yellow, red, and non-plate. The models were trained using transfer learning with COCO pre-trained weights, evaluated using precision, recall, F1-score, mAP50, and mAP50-95, and tested under real-world moderate and crowded traffic conditions. The results show that YOLOv9 consistently outperformed all other models, achieving the highest precision (97.20%), recall (96.50%), F1-score (96.85%), mAP50 (98.10%), and mAP50-95 (80.50%), with the fastest inference time of 6.8 ms per image (approximately 147 FPS). YOLOv8 ranked second, followed by YOLOv7 and YOLOv5. Across all models, the non-plate category remained the most challenging, while white and yellow plates were occasionally misclassified under low-light conditions. In conclusion, YOLOv9 is recommended for deployment in Indonesia's e-tilang system due to its best balance of accuracy and speed. Future work should expand the dataset to more diverse geographical locations, evaluate model performance under extreme weather conditions, and deploy the model on edge devices to validate real-world performance.
Comparison of FA-SSD: Performance of Feature Extractors VGG-19, MobileNetV3, and ResNet-152 for Human Body Temperature Prediction Ridho Sholehurrohman; Akmal Junaidi; Hamzah Hanif; Favorisen Rosyking Lumbanraja; Muhammad Reza Habibi
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.300-311

Abstract

Body temperature is a critical health indicator, and non-contact thermal imaging-based prediction systems are essential for early detection of infectious diseases. This study compares the performance of three FA-SSD (Feature Fusion and Spatial Attention-Based Single Shot Detector) models with different feature extractors—VGG-19, MobileNetV3, and ResNet-152—for face detection and body temperature prediction using thermal images. A subset of 520 thermal facial images from the Comprehensive Facial Thermal Dataset was used, with 80% for training, 10% for validation, and 10% for testing. Model performance was evaluated using Generalized Intersection over Union (GIoU) for detection accuracy, and Mean Absolute Error (MAE) with Mean Absolute Percentage Error (MAPE) for temperature prediction accuracy. The results showed that no single model excelled in all metrics. ResNet-152 achieved the highest average GIoU, indicating superior object detection performance. VGG-19 delivered the lowest average MAE of 0.451°C and MAPE of 1.278%, making it the best for temperature prediction. MobileNetV3 achieved the lowest minimum MAE of 0.000050°C but showed higher average errors and validation fluctuations. In conclusion, VGG-19 is recommended for clinical temperature accuracy, ResNet-152 for robust detection, and MobileNetV3 for edge deployment.
Co-Authors Akmal Junaidi Hamzah Hanif Igit Sabda Ilman Lumbanraja, Favorisen R Muhammad Reza Habibi Muhaqiqin Muhaqiqin Reza Habibi