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All Journal Jurnal Pepadun
Muhaqiqin Muhaqiqin
Department of Computer Science, Universitas Lampung
Computer Science & IT

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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.
Detection of Hate Speech in TikTok Comment Sections Using the Naïve Bayes Algorithm with Smoothing Implementation Roy Rafles Matorang Pasaribu; Didik Kurniawan; Muhaqiqin Muhaqiqin; Akmal Junaidi
Jurnal Pepadun Vol. 6 No. 3 (2025): December
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.v6i3.268

Abstract

Hate speech is a biased, antagonistic, and discriminatory expression that commonly appears on social media platforms, including TikTok. The high volume of comments and varied language styles make manual detection challenging. This research proposes a hate speech detection model using the Multinomial Naïve Bayes algorithm with smoothing to address zero-probability issues and enhance prediction performance. The dataset is split into 80% training and 20% testing portions. The model achieves an accuracy of 88.41%, with precision, recall, and F1-score showing balanced performance. A user evaluation involving 35 participants and 7,415 TikTok comments records a detection accuracy of 68.6%. The model is further implemented into a Google Chrome extension capable of real-time hate speech detection, displaying prediction probabilities and allowing user validation. This study aims to support healthier digital interactions by improving automated hate speech detection on social media.
Co-Authors Akmal Junaidi Didik Kurniawan Igit Sabda Ilman Reza Habibi Ridho Sholehurrohman Roy Rafles Matorang Pasaribu