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

Muftah Afrizal Pangestu, Hendra Bunyamin


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.

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A. Ng, Machine Learning Yearning: Technical strategy for AI engineers, in the era of deep learning, 2016.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li dan L. Fei-Fei, ImageNet: A Large-Scale Hierarchical Image Database, CVPR 2009, Miami, 2009.

A. Deshpande, A Beginner's Guide To Understanding Convolutional Neural Networks, 20 July 2016. [Online]. Available: [Diakses 28 April 2018].

A. Krizhevsky, I. Sutskever dan G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Neural Information Processing System 2012, Stateline, 2012.

Keras, Keras Documentation, [Online]. Available: [Diakses 8 April 2018].

K. He, X. Zhang, S. Ren dan J. Sun, Deep Residual Learning for Image Recognition, CVPR 2016, Boston, 2016.

F. Chollet, Xception: Deep Learning with Depthwise Separable Convolutions, CVPR 2017, Honolulu, 2017.

K. Simonyan dan A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR, San Diego, 2015.

udacity, Github, Inc., 2017. [Online]. Available: [Diakses 11 May 2018].

I. W. Suartika, A. Y. Wijaya dan R. Soelaiman, Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101, Jurnal Teknik ITS, vol. 5, no. 1, pp. 65-69, 2016

I. Krisnadi, A. Rakhmatsyah dan T. A. B. Wirayuda, Perbandingan Antara Jaringan Saraf Tiruan Multilayer Perceptron dan Jaringan Saraf Tiruan Multilayer Perceptron, 2008.

E. Alpaydin, Introduction to Machine Learning 2nd Edition, London: MIT Press, 2010.

G. Developer, Google Developer Blog, 21 September 2017. [Online]. Available: [Diakses 26 March 2018].

A. Karpathy dan J. Johnson, C231n Convolutional Neural Networks for Visual Recognition, Stanford University, [Online]. Available: [Diakses 15 April 2018]

B. Rohrer, Data Science and Robot Blog, 18 August 2016. [Online]. Available: [Diakses 28 April 2018]

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane , R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke , Y. Yu dan X. Zheng, TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, 2015.

A. F. Aslam, N. H. Mohammed dan P. Lokhande, Efficient Way of Web Development Using Python and Flask, International Journal of Advanced Research in Computer Science, vol. 6, 2015.

Pallets Team, Flask Documentation, Pallets Team , 2010. [Online]. Available: [Diakses 24 April 2018].

Python Software Foundation, Python Documentation, Python Software Foundation, 30 April 2018. [Online]. Available: [Diakses 1 May 2018]

Mathworks, Mathworks, The Mathworks, Inc, [Online]. Available: [Diakses 20 April 2018]

M. Nielsen, Neural Networks and Deep Learning, Determination Press, 2015.



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