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All Journal Jurnal Teknik Sipil Cendekia (JTSC)
yusuf yulianto
Teknik Sipil Universitas Subang
Civil Engineering, Building, Construction & Architecture

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DETEKSI KERETAKAN JALAN ASPAL MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK ari Wibowo; yusuf yulianto
JURNAL TEKNIK SIPIL CENDEKIA (JTSC) Vol 4 No 2 (2023): July
Publisher : Departement of Civil Engineering, Universitas Winaya Mukti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51988/jtsc.v4i2.132

Abstract

Road conditions determine the comfort of road users, the comfort of these road users is the responsibility of the Public Works and Spatial Planning Office in each region. Roads are of course an important aspect because roads are the main supporting factor in the social, cultural, environmental fields which are developed in order to achieve an equitable distribution of development between regions and sustainability with regional and economic development approaches. The first step that must be taken by policy makers in seeking comfort for users is to evaluate the quality of roads, including in Indonesia. The evaluation in question includes estimating repairs, required construction, estimating quality. The strategic step in making road quality evaluation steps is to detect road cracks on the surface. One of them is by implementing an intelligent system method in detecting road damage using the Convolutional Neural Network (CNN) algorithm. The input is an image of the road surface in RGB format. The image is obtained from kaggle as many as 2074 images. Based on the results of the tests and evaluations that have been carried out, it can be concluded that the system built has succeeded in producing very good data as evidenced by an accuracy rate of 92.9%.
Co-Authors ari Wibowo