Skripsi

KLASIFIKASI RAMBU LALU LINTAS MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

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Penilaian

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Classification of traffic signs is considered as one of the most important parts of the Advance Driver Assistance System (ADAS) with the main objective of reducing the number of road accidents and overcoming wrong route selection. Convolutional Neural Network (CNN) is a type of neural network that is commonly used in image data. CNN can be used to recognize and detect objects in an image. Image enhancement has an important role in improving image quality in the field of image processing, which is achieved by highlighting useful information and suppressing redundant information in images. This study uses the German Traffic Sign Recognition Benchmark dataset which contains 51,840 images of traffic signs in Germany with 43 classes. The evaluation results of the Xception architecture using Gaussian-blur with a batch size of 32 and a learning rate of 0.0001 produce a training data accuracy value of 99.99% with a test data accuracy of 98.63%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307005256	T128079	T1280792023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1280792023

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 44 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Jurusan Teknik Informatika
Jaringan Komunikasi Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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