cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Asno Azzawagama Firdaus
Contact Email
asnofirdaus@gmail.com
Phone
+6285646603602
Journal Mail Official
ijmst@abhinaya.co.id
Editorial Address
Jalan Gunung Tambora No. 1 Dasan Agung Baru, Selaparang, Mataram, Provinsi Nusa Tenggara Barat
Location
Kota mataram,
Nusa tenggara barat
INDONESIA
IJMST
Published by CV. Abhinaya Indo Group
ISSN : -     EISSN : 30903831     DOI : https://doi.org/10.64021/ijmst
Core Subject : Science,
Computer Science & IT
Indonesian Journal of Modern Science and Technology is an academic Indonesian journal that specializes in a variety of modern research in science and technology relevant to development. The journal is designed as a platform for researchers, academics, and practitioners to share their latest discoveries and innovations in various fields, including artificial intelligence, Internet of Things (IoT), information technology, robotics, electrical, biotechnology, engineering, and environmental technology. With a focus on the application of modern technology in Indonesia, the journal also covers interdisciplinary research that combines technology with social, economic, and environmental sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 21 Documents
 1   2   3 
Comparative Analysis of YOLOv8 and YOLOv11 Algorithms for Real-Time Vehicle Detection Muhammad Jordan; Firmansyah; Hasan Koten
Indonesian Journal of Modern Science and Technology Vol. 2 No. 2 (2026): May
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/

Abstract

Intelligent transportation systems required rapid and highly accurate object detection algorithms to function effectively in real-time environments. Although the You Only Look Once (YOLO) architecture evolved significantly to address these demands, comprehensive performance comparisons between its latest iterations remained sparse in the context of urban traffic. Selecting the optimal model for specific deployment constraints remained a critical challenge for developers. To address this gap, this research comparatively evaluated the performance of YOLOv8 and the newly released YOLOv11 specifically for vehicle detection tasks. The primary contribution was providing a rigorous, empirical head-to-head comparison to guide model deployment based on specific accuracy needs and hardware constraints. The methodology employed a standardized public dataset of traffic imagery containing various vehicle classes. Both nano variants, namely YOLOv8n and YOLOv11n, were trained from scratch under identical hardware environments and hyperparameter settings. Specifically, the training utilized the AdamW optimizer for fifteen epochs with a batch size of sixteen. Performance was quantitatively measured using Precision, Recall, mean Average Precision (mAP50 and mAP50-95), and computational resource allocation in terms of parameter size. The experimental results indicated a nuanced performance dynamic. YOLOv11n demonstrated a vastly superior Precision score of 0.784 compared to 0.672 for YOLOv8n, signifying its enhanced reliability with fewer false alarms due to refined spatial attention modules. Conversely, YOLOv8n outperformed YOLOv11n in Recall (0.695 versus 0.662) and mAP50 (0.746 versus 0.740). This suggested that YOLOv8n achieved faster learning convergence under constrained training conditions. Both models maintained real-time processing capabilities suitable for edge devices. In conclusion, YOLOv11n was recommended for high-accuracy traffic surveillance systems prioritizing precision, whereas YOLOv8n remained a highly viable architecture for hardware-constrained applications that prioritized high recall and aggressive object detection.

Page 3 of 3 | Total Record : 21

 1   2   3 

Filter by Year

2025 2026


Reset
Filter By Issues
All Issue Vol. 2 No. 2 (2026): May Vol. 2 No. 1 (2026): January Vol. 1 No. 3 (2025): September Vol. 1 No. 2 (2025): May Vol. 1 No. 1 (2025): January