Classification of Batik Types (Hand-Written, Stamped, and Printed) Using Gray Level Co-Occurrence Matrix Feature Extraction and Support Vector Machine
Keywords:
Batik, Computer Vision, GLCM, SVM, Image ClassificationAbstract
Batik is one of Indonesia's cultural heritages that has various production techniques, including hand-drawn batik, stamped batik, and printed batik. Despite having different production processes, these three types of batik often display similar motifs, making it difficult for the general public to distinguish them through visual observation alone. The identification process, which still relies on manual observation, also requires specialized knowledge and experience, potentially leading to errors in determining the type of batik. Therefore, a technology-based approach is needed that can perform identification more objectively and consistently. This study aims to build a classification model for batik types by utilizing digital image processing and machine learning techniques. The research stages include image preprocessing, texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and the classification process using the Support Vector Machine (SVM) algorithm. The dataset used consists of 300 batik images grouped into three classes: hand-drawn batik, stamped batik, and printed batik. The extracted texture features include contrast, correlation, energy, and homogeneity, which are then used as input attributes in the classification model. The test results show that the developed model is able to achieve an accuracy value of 90.00%, a precision of 90.25%, a recall of 90.00%, and an F1-score of 90.05%. These findings indicate that the combination of GLCM and SVM methods is effective in representing the texture characteristics of batik images and is able to distinguish batik types automatically with a good level of accuracy. This research is expected to support the use of computer vision technology in the batik identification process and contribute to efforts to preserve Indonesian culture through the application of digital technology.
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