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Deteksi Kerusakan Jalan Berdasarkan Citra Digital Menggunakan Convolutional Neural Network (CNN) Mulyana, Dadang Iskandar; Wahyudi, Ilham
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 1 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v6i1.1192

Abstract

Highways are a connection between an area or region to another destination. The rapid construction of highways in big cities is not comparable to the improvement and rearrangement of damaged roads in several areas. Most of the damaged roads are caused by heavy vehicle traffic or heavy loads with quite frequent intensity, as well as natural disasters such as floods and earthquakes. This of course disrupts the traffic system, and is quite dangerous for drivers who often pass through areas where there are many damaged roads. With these obstacles, this study aims to build a system that can detect road damage through digital image capture using the convolutional neural network method. The results of this study obtained a road damage detection accuracy value reaching 80%.

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