Deteksi Kerusakan Jalan Berdasarkan Citra Digital Menggunakan Convolutional Neural Network (CNN)

Dadang Iskandar Mulyana; Ilham Wahyudi

doi:10.35870/jimik.v6i1.1192

Vol. 6 No. 1 (2025) • Articles

Open Access

Deteksi Kerusakan Jalan Berdasarkan Citra Digital Menggunakan Convolutional Neural Network (CNN)

Dadang Iskandar Mulyana

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Ilham Wahyudi

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

DOI: https://doi.org/10.35870/jimik.v6i1.1192

Published: January 10, 2025 Pages: 294-302

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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%.

Keywords:

Detection Damaged Road CNN

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Author Biographies

Dadang Iskandar Mulyana Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Ilmu Komputer, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Ilham Wahyudi Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Ilmu Komputer, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

How to Cite

Mulyana, D. I., & Wahyudi, I. (2025). Deteksi Kerusakan Jalan Berdasarkan Citra Digital Menggunakan Convolutional Neural Network (CNN). Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(1), 294-302. https://doi.org/10.35870/jimik.v6i1.1192

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