Implementation of Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods for Recognition of Batik Bekasi Motifs Implementation of Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods for Recognition of B

Abstract

Batik is the identity and wealth of the Indonesian people in the field of culture, whose existence has been recognized by the world. However, many of the Indonesian people themselves are still unable to distinguish and recognize the motifs found on batik cloth. This is due to the large variety of motifs and patterns that are owned in different batik from each region in Indonesia. Therefore, we need a merchine leraning model that can be used to identify and classify the motifs contained in batik cloth. In this research, the method that will be used is the gray level co-occurrence matrix method which is used as a feature extraction method by utilizing five calculated parameters, namely, energy, contrast, correlation, homogeneity and entopy, as well as using the support vector machine method which is used as a motif classification method. . The batik motifs used are Bekasi batik motifs in the form of tamarind motifs, machete motifs, ondel motifs and mandar bird motifs. In this study, information on batik and the batik motifs used were obtained from a trusted source, namely the Bekasi Batik Community (KOMBAS).