Classification of cendrawasih birds using convolutional neural network (CNN) keras recognition

Abstract

Classification is part of predictive modeling and supervised learning. This method is used to determine the data class based on the previous value. In solving certain cases, there are various classification methods with varying degrees of accuracy. Convolutional Neural Network (CNN) is part of the Multilayer Perceptron (MLP) for processing two-dimensional data. CNN is also part of the Deep Neural Network and is applied to image objects. Some sources state that the classification process using images is not appropriate to be implemented in MLP. This will result in the accuracy of the method in handling certain cases. This study uses cendrawasih bird as object in the classification process to determine the accuracy of the keras recognition method. From the results of this study, a training model was conducted using 10 epochs with an accuracy and loss value of 0.0850 and 2.5658 respectively. These results indicate that MLP can successfully complete the classification process using images. These results indicate that MLP can successfully complete the classification process using images .