Analisis Performa dan Pengembangan Sistem Deteksi Ras Anjing pada Gambar dengan Menggunakan Pre-Trained CNN Model

Pangestu, Muftah Afrizal and Bunyamin, Hendra (2018) Analisis Performa dan Pengembangan Sistem Deteksi Ras Anjing pada Gambar dengan Menggunakan Pre-Trained CNN Model. Jurnal Teknik Informatika dan Sistem Informasi, 4 (2). pp. 337-344. ISSN 2443-2229

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Abstract

The main objective of this research is to develop an image recognition system for distinguishing dog breeds using Keras’ pre-trained Convolutional Neural Network models and to compare the accuracy between those models. Specifically, the models utilized are ResNet50, Xception, and VGG16. The system that we develop here is a web application using Flask as its development framework. Moreover, this research also explains how the deep learning approaches, such as CNN, can distinguish an object in an image. After testing the system on a set of images manually, we learn that every model has different performance, and Xception came out as the best in term of accuracy. We also test the acceptance of the user interface we develop to the end-users.

Item Type:	Article
Contributors:	Contribution Contributors NIDN/NIDK Email Author Pangestu, Muftah Afrizal UNSPECIFIED UNSPECIFIED Author Bunyamin, Hendra UNSPECIFIED UNSPECIFIED
Uncontrolled Keywords:	Convolutional Neural Network, Deep Learning, Flask, Image Recognition, Keras
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Information Technology > 72 Information Technology Department
Depositing User:	Perpustakaan Maranatha
Date Deposited:	28 Mar 2025 10:00
Last Modified:	28 Mar 2025 10:00
URI:	http://repository.maranatha.edu/id/eprint/33644

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