CD Skripsi

KLASIFIKASI KUALITAS PERMUKAAN JALAN RAYA MENGGUNAKAN METODE CNN BERBASIS ARSITEKTUR XCEPTION

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Highways are the main infrastructure for land transportation. The better the
condition of a highway, the better the speed and safety for drivers. One of the main
causes of accidents on highways is due to the road conditions being unsuitable for
use because of damage. Therefore, monitoring and maintaining the surface
condition of roads is very important. The quality check of highways is generally
done manually, a method that requires significant time and effort. The vast number
of roads and the manual checks that consume a lot of time and money become
obstacles in road maintenance. Therefore, the system "Highway Surface Quality
Classification Using CNN Method Based on Xception Architecture" was developed
as an alternative to perform surface quality checks on highways. This method uses
deep learning CNN with Xception transfer learning architecture. Xception was
chosen because it has a complex yet efficient architecture in terms of time usage
and high accuracy for image classification, producing accurate models with short
training times. Furthermore, several previous studies have shown that Xception
outperforms several other architectures. The use of deep learning in classifying
highway surface damage is expected to speed up and simplify the process of
monitoring road surface conditions. The model is created using a dataset with 4
classes based on the level of damage released by the Ministry of Public Works and
Public Housing (PUPR). The highest test results showed a model accuracy of
90.11% and 90% for validation.
Keywords - Road, Damaged, Xception

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1907155927

Penerbit

Pekanbaru : Universitas Riau - F. Teknik - Teknik Elektro., 2024

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1907155927

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNIK ELEKTRO

Info Detail Spesifik

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Pernyataan Tanggungjawab

GUNTUR

Versi lain/terkait

Lampiran Berkas

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