Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions

Abstract

Although Indonesian batik patterns vary by location, they usually depict local customs and cultures. Each batik has a unique quality, and to correctly identify the batik designs, you need to understand the design patterns. However, many people struggle to identify and categorize these types of motivations because they do not have the necessary knowledge, understanding, or access to sufficient information. This study used photo data to classify batik patterns into 15 different groups. Batik Kawung, Megamendung, Lasem, Pole, Machete, Gills, Nutmeg, Karaswasih, Cendrawasih, Geblek Renteng, Bali, Betawi and Dayak are all included in this category. 1,350 images were used in the investigation. Google supports data collection. To provide the highest level of precision and to evaluate how image dimensions affect the classification of batik designs, this study uses convolutional neural networks (CNNs). The results of this study show that multilayer perceptron (MLP) is a well-liked deep learning method for data classification, especially in domains where picture classification is involved. The size of the images utilized affects the accuracy of the convolutional neural network (CNN) algorithms. The results showed that the testing using comparisons of 60%, 30% and 10% training data resulted in a 01.89% loss reduction of 1.89% and a 100% improvement in accuracy