Classification of Batik Motif Using Transfer Learning on Convolutional Neural Network (Cnn)

Abstract

The number of batik motifs in Indonesia is not comparable to the knowledge possessed by the Indonesian people about batik motifs. The diversity of batik motifs can be a problem because classifying them can only be done by those who are familiar with batik in depth, both the pattern and the philosophy behind the motif, most of which are elderly people. To classify batik accurately and quickly is to use image classification technology. In this study, data were obtained from the previous researchers' GitHub repository, google images, and camera shots with a total dataset of 3,534 images. The data only focused on five batik motifs, namely Ceplok, Kawung, Parang, Megamendung, and Sidomukti. Before the batik motif is processed, preprocessing is carried out to obtain various quality data. Then the dataset was trained using the CNN model then the results were retrained using the VGG-16 and Xception Transfer Learning models. The researcher made several model scenarios, namely the CNN model without Transfer Learning and the model with Transfer Learning which took into account the effect of the learning rate values ​​of 0.0004 and 0.0001. Therefore, the results of the CNN model without Transfer Learning (M0) obtained training accuracy results of 89.64%. While the results of the model with the best Transfer Learning is the M2 model (CNN + VGG-16, learning rate = 0.0001) with an accuracy of 91.23%, a loss of 24.48%, and the test results obtained an accuracy of 89%. Based on the results of the classification method, it can be concluded that the CNN model with Transfer Learning performs classification better in terms of accuracy and computation time than the CNN model.

Rice Disease Recognition Using Transfer Learning Xception Convolutional Neural Network

“…Its ability to understand the local context in images provides advantages, especially when applied to plant disease detection. CNNs also have a large learning capacity and transfer learning capabilities improve model performance even with limited datasets [15]- [17]. Thus, CNN becomes a more effective and adaptive choice in handling the structural and spatial complexity of image data related to diseases in rice plants.…”

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

“…The M2 model, which integrates the convolutional neural network (CNN) and VGG-16, achieved a training precision of 91.23% with a loss of 24.48% and a training rate of 0.0001. Furthermore, the M2 model exhibited a training precision rate of 91.23% when assessed using test data [10].…”