Pattern Recognition of Batik Madura Using Backpropagation Algorithm

Abstract

Since October 2, 2009, UNESCO has acknowledged batik as one of Indonesia's intellectual properties. Throughout the archipelago, distinct and diverse batik motifs have emerged and been produced with the passage of time; Madura batik is one of them. The Backpropagation Algorithm is used to recognize Madura Batik Patterns in this research. Bunga Satompok, Manuk Poter, Pecah Beling, Rumput Laut, and Sekar Jagat are the motifs used in this study. To begin, resize the image to 200 × 200 pixels and convert it to a grayscale image. The Gray Level Co-occurrence Matrix (GLCM) approach is used to extract image features, and the Backpropagation Algorithm is used to recognize them. With GLCM, the angle orientations utilized in the feature extraction process are 0, 45, 90, and 135 degrees. There are 1, 3, and 5 hidden layers used throughout the training process, with hidden neurons in each layer of 8, 16, and 32. The highest accuracy is achieved when five hidden layers with 32 hidden neurons and one hidden layer with 32 hidden neurons in each layer are used in the testing process, which is 98 percent.