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KLASIFIKASI RAS ANJING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

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Penilaian

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Each dog breed has different characteristics and maintenance methods. It is very important for dog keepers to know the breed of their pet dog, because it can affect the dog's physical health. There has been no research that has classified dog breeds with the MobileNet architecture using the dataset used in this study. Therefore, this study aims to build software that can classify dog breeds from dog facial image input. This software uses the Convolutional Neural Network method with the MobileNet architecture because it has a small size but provides a fairly high accuracy. Classification is done based on the front, right, and left side of the dog's face. The dataset used is image data with a total of 7946 training data, 700 test data, and 700 validation data. Experiments conducted in this study resulted in the highest accuracy rate of 96% from the combination of a lower learning rate and more epochs. Based on the analysis carried out, it is assumed that the similarity of images and patterns between classes in the dataset affects the accuracy of image recognition.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002214	T74483	T744832022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T744832022

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xix, 76 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

Content Type

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Media Type

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Carrier Type

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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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