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All Journal Journal of Applied Data Sciences Prioritas : Jurnal Pengabdian Kepada Masyarakat
Maria Irmina Prasetiyowati
Universitas Multimedia Nusantara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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RANCANG BANGUN WEBSITE E-LIBRARY PADA TUNAS MULIA MONTESSORI SCHOOL Adhi Kusnadi; Fenina Adline Twince Tobing; Yaman Khaeruzzaman; Dennis Gunawan; Wirawan Istiono; Alexander Waworuntu; Maria Irmina Prasetiyowati
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol 5 No 02 (2023): EDISI SEPTEMBER 2023
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/prioritas.v5i02.812

Abstract

A website is a collection of pages that contain specific information and can be accessed by anyone, anytime easily through an internet connection. Websites have become one of the facilities of the internet that can make life easier. The various positive impacts provided by website technology offer convenience to users. One of them is an E-library to obtain the necessary information for research or simply for reading. Almost all schools have implemented this technology, and Tunas Mulia Montessori School is also eager to build an E-library. The realization of this goal is assisted by Multimedia Nusantara University, which has the technological capabilities. The website development method used is the SDLC (System Development Life Cycle) concept, which is a process used by system analysts to develop an information system. The presence of the E-Library website has greatly impressed Tunas Mulia. This can be seen from the school's enthusiasm during the presentation of the results and the survey, where all questions were answered with satisfaction and great satisfaction
EagleEyes: An Artificial Intelligence-Based Approach for Automatic Traffic Violation Detection Using Deep Learning Windu Gata; Muhammad Haris; Maria Irmina Prasetiyowati; Sony Harianto
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 Adhi Kusnadi Dennis Gunawan Fenina Twince Tobing Istiono, Wirawan Muhammad Haris Sony Harianto Waworuntu, Alexander Windu Gata Yaman Khaeruzzaman