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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MEDIA KOMUNIKASI TEKNIK SIPIL Jurnal Ilmiah Desain & Konstruksi Prosiding PESAT Rekayasa Sipil Jurnal Teknik Sipil Jurnal Rekayasa Sipil Jurnal Penelitian Pendidikan IPA (JPPIPA) Syntax Literate: Jurnal Ilmiah Indonesia ILKOM Jurnal Ilmiah Borneo Engineering: Jurnal Teknik Sipil Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Teknik Sipil
Ida Ayu Ari Angreni
Gunadarma University
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Physics Social Sciences Transportation Other

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Road Defect Assessment Algorithm on Flexible Pavement Ida Ayu Ari Angreni; Diyanti Diyanti
Jurnal Penelitian Pendidikan IPA Vol 11 No 3 (2025): March
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i3.10471

Abstract

This study aims to examine the condition of road pavement mechanically which requires large, time-consuming, impractical, and can only identify one type of road damage. The development of digital technology, then identifying the type of damage can be done with an algorithm or method to detect and analyze the type of road damage quickly and accurately. The purpose of the study is to identify the value of road damage with the visual method of Dirgolaksono and Mochtar, create a model of a road damage assessment algorithm based on digital imagery, and apply the digital image method to the road section being reviewed. The research method with the initial step of the algorithm process is taking pictures using a type of digital camera, so that a digital image is produced which is then processed using Matlab R2016a. The results obtained are the classification of road damage and the damage value of the road section obtained by visual road damage and digital imagery accurately. Validation is carried out with a strong correlation between visual and digital damage, which means that there is no difference between the visual damage value and the digital image damage value
Automated Hyperparameter Optimization of Lightweight YOLO11s for Efficient Road Crack Detection Angreni, Ida Ayu Ari; Diyanti, Diyanti; Valentine, Vega
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.2894.138-150

Abstract

Automatic road crack detection plays an essential role in infrastructure maintenance, where rapid and accurate visual inspection is required under real-world conditions. Although deep learning–based detection models have demonstrated promising performance, many existing approaches rely on computationally intensive architectures or require manual hyperparameter tuning, which limits their efficiency and real-time applicability. Moreover, the integration of lightweight detection models with automated hyperparameter optimization remains relatively underexplored.This study proposes an efficient road crack detection framework based on a lightweight YOLO11s architecture enhanced through automated hyperparameter optimization using Optuna on the DeepCrack dataset. The proposed methodology includes image preprocessing through data augmentation, normalization, and resizing to improve model robustness. Subsequently, key hyperparameters including learning rate, weight decay, dropout rate, and optimizer selection are automatically optimized to obtain the best model configuration. Experimental results indicate that the optimized YOLO11s model achieves a precision of 90.4%, recall of 86.8%, mAP@0.5 of 89.8%, and mAP@0.5:0.95 of 63.6% after 25 optimization trials. These results demonstrate that automated hyperparameter optimization can significantly improve detection performance while maintaining computational efficiency. The main contribution of this study lies in the systematic integration of automated hyperparameter tuning within a lightweight YOLO-based framework, providing a resource efficient and accurate solution suitable for real-time and large-scale road damage monitoring
Hybrid automated road crack segmentation using morphological operations and boundary tracing Ida Ayu Ari Angreni; Diyanti Diyanti; Vega Valentine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2181-2191

Abstract

Cracks in the road surface are one of the early indicators of structural damage that has an impact on safety and infrastructure maintenance costs. Accurate early detection is a challenge in complex visual conditions such as uneven lighting and varied asphalt textures. This study proposes an efficient and fully automated hybrid segmentation method to detect cracks in road surface imagery. This method consists of several main stages: image enhancement using contrast limited adaptive histogram equalization (CLAHE), initial segmentation through a combination of Otsu's thresholding, adaptive Gaussian thresholding, and Canny edge detection, followed by mask enhancement with morphological operations (closing, opening, and erosion). The DeepCrack dataset is used as a source of test data. The evaluation results showed high performance with detection accuracy reaching 95.82%. These findings show that the proposed method is not only precise and sensitive, but also adaptive to visual variation without the need for manual training or parameters. A major novelty lies in the integration of three classic segmentation methods in one morphology pipeline that is computationally lightweight yet competitive, making it potential for real-world applications of automated inspection systems.
Co-Authors A.A. Ketut Agung Cahyawan W Adisasmita, Sakti Adji Alma Hastari Salsabila Chintya Kusuma Pertiwi Darmini Darmini Dedi Dedi Diyanti Diyanti Diyanti Diyanti Esab Rachmat Tama Hendi Kurniawan I. B. Sufitriasa Jennie Kusumaningrum Jiant Vially Juzailah Tri Handayani Khofiyah, Ovie Lativatul M Isran Ramli M. Amir Salipu Rika Apriani Silaban, Merdy Evalina Sri Oktaviani Sumarni Hamid Aly Tuti Rahmawati Vega Valentine Vega Valentine Yuliana Yuliana