KLASIFIKASI GAMBAR DATASET FASHION-MNIST MENGGUNAKAN DEEP CONVOLUTIONAL NEURAL NETWORK
Abstract
This study uses the Fashion-MNIST dataset from Zalando Research, which consists of 60000 images for training and 10000 images for testing, each image size 28x28 pixels. The deep learning method used is the Deep Convolutional Neural Network (DCNN), with the activation function relu on the inside of the layer and softmax at the end of the layer. Test accuracy without using dropout gets 92.69% with loss of 0.445 and using dropout gets 92.84%, loss 0.206.
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References
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[10.] Susilawati (2017). Algoritma Restricted Boltzman Machines (RBM) untuk Pengenalan Tulisan Tangan. Seminar Nasional Teknologi Informatika, 140-148.
[11.] Xiao, H., Rasul, K., Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747
[12.] Yu, D., Wang, H., Chen, P., & Wei, Z. (2014). Mixed pooling for convolutional neural networks. In Proceedings of the 9th International Conference on Rough Sets and Knowledge Technology (pp. 364–375). Berlin: Springer.
[2.] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
[3.] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989a). Handwritten digit recognition with a back-propagation network. In D. S. Touretzky (Ed.), Advances in neural information processing systems, 2 (pp. 396–404). Cambridge, MA: MIT Press.
[4.] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989b). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541–551.
[5.] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444
[6.] Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (pp. 807–814). N.p.: International Machine Learning Society.
[7.] Ranzato, M. A., Huang, F. J., Boureau, Y., & LeCun, Y. (2007). Unsupervised learning of invariant feature hierarchies with applications to object recognition. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–8). Los Alamitos, CA: IEEE Computer Society.
[8.] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556.
[9.] Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R.(2014). Dropout: Asimple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
[10.] Susilawati (2017). Algoritma Restricted Boltzman Machines (RBM) untuk Pengenalan Tulisan Tangan. Seminar Nasional Teknologi Informatika, 140-148.
[11.] Xiao, H., Rasul, K., Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747
[12.] Yu, D., Wang, H., Chen, P., & Wei, Z. (2014). Mixed pooling for convolutional neural networks. In Proceedings of the 9th International Conference on Rough Sets and Knowledge Technology (pp. 364–375). Berlin: Springer.
Published
2019-05-22
How to Cite
Priyowidodo, S. (2019, May 22). KLASIFIKASI GAMBAR DATASET FASHION-MNIST MENGGUNAKAN DEEP CONVOLUTIONAL NEURAL NETWORK. JiTEKH, 7(1), 34-38. https://doi.org/https://doi.org/10.35447/jitekh.v7i01.15
Section
JITEKH
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