Classification of Tasikmalaya batik motifs using convolutional neural networks

This paper presents a study on the classification of traditional Tasikmalaya batik motifs using convolutional neural networks (CNN). The experiments revealed that the high complexity of batik motifs significantly impacted model performance, as the handling of each class influenced the overall results. Initial experiments with the original dataset demonstrated suboptimal performance, characterized by accuracy and validation curves indicating overfitting, with only 75% accuracy achieved at a learning rate of 0.001, a batch size of 32, and 50 epochs. To enhance performance, we implemented data segmentation, data augmentation, optimized the choice of the best optimizer, utilized an optimal architecture, and conducted hyperparameter tuning. The best-performing model was trained on data subjected to specific preprocessing for each class, using the Adam optimizer with hyperparameter tuning set to a learning rate of 0.001, a batch size of 32, and 50 epochs. In the hyperparameter tuning experiment with the visual geometry group network (VGGNet) architecture, it was shown that there is an improvement in the prediction of the kumeli class, achieving an accuracy of 100%.