Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MEDIA KOMUNIKASI TEKNIK SIPIL ILKOM Jurnal Ilmiah
Diyanti Diyanti
Gunadarma University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Automated Hyperparameter Optimization of Lightweight YOLO11s for Efficient Road Crack Detection Ida Ayu Ari Angreni; Diyanti Diyanti; Vega Valentine
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
Optimalisasi Konservasi Air Melalui Sumur Resapan Untuk Mengatasi Dampak Urbanisasi Studi Kasus di Perumahan Nuansa Alam Cilebut Asri Diyanti Diyanti; Nur Mila Sari; Rehulina Apriyanti
MEDIA KOMUNIKASI TEKNIK SIPIL Volume 32, Nomor 1 (2026)
Publisher : Department of Civil Engineering, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/mkts.v32i1.82747

Abstract

The rapid expansion of the Nuansa Alam Cilebut Asri Housing Area has transformed 6 hectares of agricultural land into residential areas, resulting in the loss of natural infiltration zones due to increased impervious surfaces from roads and buildings. This land-use change has altered the hydrological characteristics of the area by increasing surface runoff, intensifying inundation risks during extreme rainfall events, and reducing groundwater recharge capacity. This study evaluates water conservation measures to mitigate inundation impacts through the implementation of infiltration wells. A quantitative approach was applied by integrating soil permeability testing, frequency analysis of 10-year historical rainfall data from three observation stations, and assessment of suitable water conservation structures. The Log Pearson Type III distribution was selected based on the goodness-of-fit test for a 2-year return period, producing a design rainfall of 109.107 mm/day and a design runoff discharge of 0.284 m³/s. The analysis indicates that the runoff can be effectively controlled through the construction of 360 infiltration wells with a diameter of 1 m, a depth of 3 m, a storage capacity of 2.36 m³, and a filling duration of 2 hours and 20 minutes. The proposed green infrastructure approach significantly reduces surface runoff and enhances the hydrological resilience of the area through sustainable groundwater recharge.
Co-Authors Ida Ayu Ari Angreni Nur Mila Sari Rehulina Apriyanti Vega Valentine