Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal ELINVO (Electronics, Informatics, and Vocational Education) Journal of Information Engineering and Technology
Ardianto, Widya
Unknown Affiliation
Computer Science & IT Education Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pengembangan Aplikasi Desain Grafis Berbasis Web dengan Framework React-Konva dan Generative AI Ardianto, Widya; Nurkhamid
Journal of Information Engineering and Technology Vol. 3 No. 2 (2025): September 2025
Publisher : Department of Electronics and Informatics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jiety.v3i2.2161

Abstract

Penelitian ini bertujuan mengembangkan aplikasi desain grafis berbasis web menggunakan framework React-Konva dengan Generative AI serta menilai kualitasnya berdasarkan ISO25010:2023. Pengembangan aplikasi menggunakan metodologi Research and Development (RnD) dengan model pengembangan perangkat lunak Feature-Driven Development. Pengujian kualitas dilakukan pada aspek functional suitability melalui validasi ahli dan performance efficiency menggunakan Chrome Dev Tools. Hasil penelitian menunjukkan aplikasi berhasil dikembangkan dengan reactive dan declarative binding serta integrasi Generative AI. Pengujian menghasilkan skor functional suitability 100% dengan kategori "Sanget Baik", dan performance efficiency menunjukkan penggunaan CPU rata-rata 10%, memori 20,2-37,5 MB, frame rate 133,9 fps, dan response time 132ms. Aplikasi telah memenuhi standar kualitas yang baik, namun memerlukan optimasi untuk mencapai response time di bawah 100ms.
Optimizing YOLO Models for Enhanced Road Damage Detection: A Performance Comparison of YOLOv5 and YOLOv8 Indrihapsari, Yuniar; Wijaya, Danang; Ardy, Satya Adhiyaksa; Siswanto, Ikhwan Inzaghi; Ardiansyah, Dhista Dwi Nur; Ardianto, Widya
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 2 (2025): November 2025 (In-Press)
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i2.88919

Abstract

Accurate road damage detection is vital for ensuring road safety and infrastructure maintenance. This study evaluates and compares the performance of four YOLO models—YOLOv5-S, YOLOv5-M, YOLOv8-S, and YOLOv8-M—for detecting road damage types such as Alligator Cracks, Longitudinal Cracks, Transverse Cracks, Potholes, and Lateral Cracks. The models were trained on a combined dataset from GRDDC 2020 and the Ministry of Public Works and Housing (PUPR) Republic of Indonesia, addressing challenges like class imbalance and diverse road conditions. Results show that YOLOv8-M achieved the highest mAP@0.5 (0.412), excelling in precision and recall for prominent damage types, making it the most reliable for high-accuracy applications. YOLOv5-M balanced precision and recall, while YOLOv5-S prioritized recall, making it suitable for detecting widespread damage. However, all models struggled with less prominent types, such as Lateral Cracks, due to class imbalance. Misclassifications were common, with the "Background" class absorbing predictions from other categories. This study highlights the strengths and limitations of each model, offering insights into improving road damage detection through better feature extraction, expanded datasets, and optimized architectures. These findings provide a foundation for deploying automated deep learning-based road damage detection systems to enhance infrastructure management.
Co-Authors Ardiansyah, Dhista Dwi Nur Ardy, Satya Adhiyaksa Nurkhamid Siswanto, Ikhwan Inzaghi Wijaya, Danang Yuniar Indrihapsari