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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Journal of Applied Data Sciences
Harianto, Sony
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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The Improving Cross-Project Software Defect Prediction with CORAL-Based Domain Adaptation and Ensemble Learning Harianto, Sony
Telematika Vol 22 No 1 (2025): Edisi Februari 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i1.14939

Abstract

Abstract—This study presents a cross-project software defect prediction (CSDP) framework combining feature harmonization, CORAL-based domain adaptation, SMOTE balancing, PCA reduction, and ensemble classifiers: Random Forest, Logistic Regression, XGBoost, AdaBoost, and VotingClassifier. Evaluations on five AEEEM datasets (JDT, EQ, PDE, Lucene, Mylyn) in both single-source and multi-source settings show consistent improvements over baseline methods. While not outperforming deep learning models, the approach remains practical and interpretable for real-world CSDP tasks.
EagleEyes: An Artificial Intelligence-Based Approach for Automatic Traffic Violation Detection Using Deep Learning Gata, Windu; Haris, Muhammad; Prasetiyowati, Maria Irmina; Harianto, Sony
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1097

Abstract

Rapid urbanization and the advancement of smart city programs in Indonesia necessitate intelligent, automated solutions for traffic monitoring and law enforcement. This study introduces EagleEyes, an artificial intelligence–based framework designed for automatic detection of multiple traffic violations by integrating the YOLOv8 deep learning architecture with Optical Character Recognition (OCR) for vehicle license plate identification. YOLOv8 was selected due to its anchor-free design, decoupled detection head, and enhanced feature fusion modules, which collectively improve detection accuracy, convergence speed, and small-object recognition compared to YOLOv5 and YOLOv7, while maintaining lightweight computational efficiency suitable for real-time applications. The proposed system was trained on a multi-class dataset representing common Indonesian violations, including seat belt non-compliance, helmet absence, motorcycle overcapacity, and unreadable license plates. Experimental results demonstrate robust performance, achieving a precision of 0.91, recall of 0.92, and mean average precision (mAP@0.5) of 0.96 at the optimal epoch, with an average inference speed of 25 frames per second and total training time of approximately 15 minutes on an NVIDIA RTX GPU. The OCR module attained an average recognition accuracy of 98.7%, although its performance decreased for vehicles captured beyond a five-meter distance due to reduced clarity and illumination inconsistencies. Implemented as a web-based application using the Flask framework, EagleEyes enables flexible browser-based visualization, and can be seamlessly integrated into Indonesia’s Electronic Traffic Law Enforcement (ETLE) infrastructure. Overall, the system demonstrates high potential to enhance smart city traffic management through scalable, AI-driven, and ethically responsible automation.
Co-Authors Gata, Windu Maria Irmina Prasetiyowati MUHAMMAD HARIS