Coastal Batik Motifs Identification Using K-Nearest Neighbor Based on The Grey Level Co-occurrence Method

Abstract

Indonesia is a country rich in natural, cultural, and tourism resources. One of the famous human cultural heritage in Indonesia is batik. Batik has unique motifs that are very diverse so it is difficult to recognize the in certain classes. This research was conducted to classify coastal batik, especially Tegal batik, Pekalongan batik, and Cirebon batik so that it can help facilitate the introduction and understanding of coastal batik when compared to another batik, such as Yogyakarta batik. The method used is Grey Level Co-occurrence Matrices to extract texture features, while, to determine the proximity of the test image to the training data using the K-Nearest Neighbor method, the calculation of the distance to be used is the Euclidean Distance and Manhattan Distance based on the texture characteristics of the batik image obtained. In this study, the highest score was obtained at 64% for Euclidean Distance and 66% for Manhattan Distance at k=15