Penerapan Convolutional Neural Networks Menggunakan Edge Detection Untuk Identifikasi Motif Jenis Batik - Lis Utari; Ammar Zulfikar (TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains, 2023)
Batik is the work of the Indonesian nation which is a combination of art and technology by the ancestors of the Indonesian people. UNESCO designated batik as a Humanitarian Heritage for Masterpieces of the Oral and Intangible Heritage of Humanity, followed by a presidential decree on 2 October 2009 which was designated as Indonesia's National Batik Day. In maintaining the existence of batik in the era of technological advances, they have utilized Artificial Intelligence and Machine Learning technologies. Even though research on batik motifs has become a common topic, there are still many mistakes found during the process of identifying motifs. As is well known, Indonesia has various types of traditional batik which have very diverse colors, patterns and motifs. This is the main problem in the identification of batik motifs, where it is found that many batik motifs adopt or have similarities in either pattern or color to other batik motifs, giving rise to thin predictions when identified with other motifs. This study uses the Convolutional Neural Networks (CNN) method using Edge Detection to identify batik motifs. Using the main dataset of 1106 images divided into 4 classes, namely Kawung, Megamendung, Merak Ngibing, and Parang motifs. The research is focused on comparing the results of prediction effectiveness on the CNN model with Canny edge detection (CNN-Canny) and the CNN model with Sobel edge detection (CNN-Sobel). The training process for each model is applied the same configuration to the dataset with a ratio of 8:2. Then in the model augmentation the functions of random flip, random zoom, and random invert are applied. The results obtained from learning the machine learning model at the testing and validation stage on the CNN-Sobel model obtained an accuracy of 91.2% in the training process and 91.8% in the validation process. Meanwhile, CNN-Canny got 90.5% accuracy in the training process and 86.2% in the validation process. The results of the comparison of the performance of the two models which are mapped to the confusion matrix table on 16 data testing in the form of images of batik motifs that have never been studied by each model show that the CNN-Sobel model can work more optimally than the CNN-Canny model with an accuracy comparison percentage of 94%. compared to 76% in the process of identifying batik motifs.