Influence of gray level co-occurrence matrix for texture feature extraction on identification of batik motifs using k-nearest neighbor

Abstract

Batik is one type of fabric that is unique because it has a special motif, in Indonesia itself batik is unique because it has certain motifs that are made based on the culture from which batik was made. This study aims to examine the effect of the texture feature extraction method on the identification of batik motifs from five major islands in Indonesia. The method used in this study is the Gray Level Co-occurrence Matrix as the texture feature extraction of batik motifs to obtain good batik motif identification accuracy results and to determine the value of the proximity of the training data and image testing of batik motifs, the K-Nearest Neighbor classification method will be used based on texture feature extraction value obtained. In this experiment, 5 experiments will be carried out based on angles 0 degrees, 45 degrees, 90 degrees, 135 degrees, and 180 degrees using the values of k is1, 3, 5, and 7. The confusion matrix will be used to calculate the accuracy level of the K-Nearest Neighbor classification. From the results of experiments carried out using training data as many as 607 images and testing as many as 344 images in five classes used with angles of 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, and values of k are 1, 3, 5, and 7, getting the highest accuracy results is at an angle of 135 degrees and 180 degrees with a value of k is 1 of 89.24% and the lowest is at an angle of 90 degrees with a value of k is 3 of 67.44%. This shows that the Gray level co-occurrence matrix method is good for extracting the texture features of batik motifs from five major islands in Indonesia, it is evidenced by the results of the average accuracy of the classification obtained.