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All Journal TEKNIK Jurnal Pendidikan Teknologi dan Kejuruan Indonesian Journal of Electrical Engineering and Informatics (IJEEI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization JST ( Jurnal Sains Terapan ) JURNAL INTEGRASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-Dinamika: Jurnal Pengabdian Kepada Masyarakat JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) Jurnal Teknik Mesin dan Mekatronika (Journal of Mechanical Engineering and Mechatronics) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal Rekayasa elektrika
Hary Oktavianto
Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Transportation Other

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YOLOv11-Based Detection of Indonesian Traffic Signs: Transfer Learning vs. From-Scratch Training Ramadhan, Ibnu Cipta; Hendriawan, Akhmad; Oktavianto, Hary
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9718

Abstract

Traffic sign detection is a fundamental component in intelligent transportation systems (ITS), autonomous driving, and advanced driver assistance systems (ADAS), enabling vehicles to interpret road conditions and enhance safety. Developing robust traffic sign detection models for specific regions requires high-quality, well-annotated local datasets, which are often challenging and costly to create. Even when such datasets are available, training deep learning models from scratch demands substantial computational resources and time. This study compares models trained from scratch and those using transfer learning based on the lightweight YOLOv11s architecture on an Indonesian traffic sign dataset. Evaluations using precision, recall, mean Average Precision at IoU 0.5 (mAP@0.5), and mean Average Precision across IoU thresholds 0.5 to 0.95 (mAP@0.5:0.95) demonstrate that the transfer learning model consistently outperforms the from-scratch model across all metrics. These findings highlight the effectiveness and efficiency of transfer learning for developing accurate and practical traffic sign detection systems adapted to local contexts.
Comparative Study of CNN Architectures for Real-Time Audio-Based Car Accident Detection on Edge Devices Ilahi, Ahmada Haiz Zakiyil; Irwansyah, Arif; Oktavianto, Hary
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2985

Abstract

Traffic accidents often result in fatalities for both drivers and bystanders. Traditionally, accident information relies heavily on community reports, which can delay the provision of victim assistance. To address this issue, a system capable of detecting accidents responsively in various weather conditions and traffic densities is necessary. One approach involved using audio analysis techniques to evaluate collision sounds. Thus, this study proposed an audio classification system for detecting car accidents using Convolutional Neural Networks (CNNs). The system’s performance was evaluated on personal computers and edge devices, such as the Raspberry Pi 4 and NVIDIA Jetson Nano, to compare inference times and power consumption. To enhance the dataset, segmentation and augmentation techniques were applied before converting the audio data into a 2D Mel-spectrogram. The dataset was then trained and assessed with four CNN architectures: custom sequential, custom with shared input layer, transfer learning EfficientNetB0, and transfer learning MobileNetV2. Both original and Lite models were deployed on experimental devices. Results showed that the custom CNN model had faster inference times across devices in both original and lite forms, though it had a 4% increase in the false positive rate. The Lite MobileNetV2 model recorded the fastest inference time on edge devices at 86 ms. Jetson Nano exhibited faster inference times compared to Raspberry Pi 4. However, Raspberry Pi 4 showed a minor increase in power consumption of 0.6 watts during inference. In future work, this system can be tested in real-time environments using embedded systems to evaluate its robustness against noise and varying environmental conditions.
Co-Authors Abdillah Aziz Muntashir Abdullah Aziz Muntashir Achmad Purnomo Wijaya Amang Sudarsono, Amang angga wahyu aditya Apriyanto, R. Akbar Nur APRIYANTO, RADEN AKBAR NUR Aulia, Fira Bagus Nugraha Deby Ariyadi Bambang Sumantri BASUKI, GAMAR Bima Sena Bayu Dewantara Dadet Pramadihanto Era Purwanto Fakhruddin, Hanif Hasyier Fatekha, Rifqi Amalya GAMAR BASUKI Gamar Basuki Gigih Prabowo Handri Toar HANIF HASYIER FAKHRUDDIN Hanif Hasyier Fakhruddin Hendriawan, Akhmad Idris Winarno Ilahi, Ahmada Haiz Zakiyil Irwansyah, Arif Izzulhaq, Muhammad Arya Kadek Reda Setiawan Suda Kisron Kisron M Udin Harun Al Rasyid, M Udin Harun Mentari Putri Jati MOCHAMAD ARI BAGUS NUGROHO Niam Tamami R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Oktav Yama Hendra Ramadhan, Ibnu Cipta Ridwan Ridwan Rusli, Muhammad Rizani Rusminto Tjatur Widodo Setiawan Suda, Kadek Reda Sri Muntiah Andriami Wahjoe Tjatur Sesulihatien Wirama, I Made Adiswara