KLASIFIKASI RAS ANJING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

ALMIRA, EDNAGEA and Utami, Alvi Syahrini and Marieska, Mastura Diana (2022) KLASIFIKASI RAS ANJING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Ednagea Almira
Date Deposited:	22 Jul 2022 05:29
Last Modified:	22 Jul 2022 05:29
URI:	http://repository.unsri.ac.id/id/eprint/74483

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