DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK TO CLASSIFICATION THE OBJECT RECOGNITION USING MXNET

ABSTRACT

Batik, as one of the ancestral cultural heritage of Indonesian, has many kinds of patterns. The pattern of batik can not be separated from the intherent elements of the origin region of the maker. Knowledge about the recognition of the pattern of Batik may only be owned by certain people who have expertise in related fields, such as the field of Batik drawing which is not everyone can recognize the pattern of Batik. But the development oh the era and the increasing need for information encourage people to develop new technologies for management and information can be done easily and quickly. Deep learning is a neural network model that has recently started to develop, and it has shown good results in improving the accuracy of object recognition or other cases. This study aims to find out how deep learning, with one of its methods called the Convolutional Neural Network (CNN), does the classification of the recognition of the patterns of batik. Before designing CNN, Batik images must first be resized to greyscale, so the resulth of the conversion can be input into CNN. The test result on the built system shows that the convolutional neural network model with two convolution layers and two fully connected layers with 100 epoch gives the best accuracy result for four classes of batik 53.75%.

Keywords : Object Recognition, Batik, Deep learning, Convolutional Neural Network