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Image of KLASIFIKASI RAMBU LALU LINTAS MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Skripsi

KLASIFIKASI RAMBU LALU LINTAS MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Hisbullah, Muhammad Azka - Personal Name;

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Penilaian anda saat ini :  

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%.


Availability
Inventory Code Barcode Call Number Location Status
2307005256T128079T1280792023Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
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
-
Classification
004.650 7
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Jurusan Teknik Informatika
Jaringan Komunikasi Komputer
Specific Detail Info
-
Statement of Responsibility
SEPTA
Other version/related

No other version available

File Attachment
  • KLASIFIKASI RAMBU LALU LINTAS MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK
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