KLASIFIKASI RAS KUCING DENGAN CONVOLUTIONAL NEURAL NETWORK PADA CITRA MULTI OBJEK

QATRUNNADA, NAURA and Fachrurrozi, M. and Utami, Alvi Syahrini (2021) KLASIFIKASI RAS KUCING DENGAN CONVOLUTIONAL NEURAL NETWORK PADA CITRA MULTI OBJEK. Undergraduate thesis, Sriwijaya University.

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Abstract

Cat is one of the most popular pets. There are many cat breeds with unique characteristic and treatment for each breed. A cat owner can have more than one cat, either the same breed or different breeds. But not all cat owners know the breeds of their cats. Computers can be trained to recognized cat breeds, but there are many challenges for computers because it limited by how much they have been trained and programmed. In recent years, a lot of research about image classification has been done before and got various result, but most of the data used in previous research were single object images. Therefore, this study of cat breeds classification would be conducted with Convolutional Neural Network (CNN) in the Multi-Object images. This method was chosen because it had good classification results in the previous studies. This study used 5 breeds of cats with every breed having 200-3200 images for training. The test results were measured using confusion matrix, obtaining the precision, recall, f1 score and accuracy of 100% on multi-object images with 2 objects and 3 objects. On images with 4 objects achieved the precision, recall, f1 score and accuracy value of 89%, 87%, 87% and 95%. While the value of precision, recall, f1 score and accuracy on images with 5 objects get 87%, 86%, 86% and 94%, respectively.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Citra, Convolutional Neural Network, Citra Multi Objek
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Naura Qatrunnada
Date Deposited:	20 Jan 2022 09:00
Last Modified:	20 Jan 2022 09:00
URI:	http://repository.unsri.ac.id/id/eprint/61998

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