DEEP LEARNING ARCHITECTURE BASED ON CONVOLUTIONAL NEURAL NETWORK (CNN) IN IMAGE CLASSIFICATION

Abstract

In current technological developments, Deep Learning is one of the most popular studies today, especially in the fields of machine learning and computer vision, GPU Acceration Technology is one of the reasons for the development of Deep Learning. Deep Learning has a very good ability to solve classic problems in the field of computer vision, one of which is in the case of object classification in images. one of the deep learning methods that is often used in image processing is the Convolution Neural Network (CNN) which is a development of the Multi Layer Perceptron method. This study uses the CNN architecture which consists of a convolution layer, as well as a fully connected layer, and will also determine the appropriate Optimizer and Loss function for CNN. The implementation of this method uses Google Colab (Tensorflow and Keras) with the Python programming language. In the training process using CNN, setting the number of epochs is done to improve accuracy in image classification, in the first scenario using epoch 20 produces an average accuracy of 99.45 with a loss value of 1.66. In the second scenario using epoch 15 produces an average accuracy value of 99.00 with a loss value of 2.92. then in the third scenario with a number of epochs 10 it produces an average accuracy value of 95.55 with a loss value of 95.55, while in the last scenario with a number of epochs 5 it produces an average accuracy value of 73.6 with a loss value of 51.92. From the 4 trial scenarios using the CNN method gives effective results and produces a fairly good accuracy value with an average accuracy and loss value of 99.99%. As well as the results of an average loss of 4.