Quadratic support vector machine for the bomba traditional textile motif classification

Abstract

The Bomba textile is one of the textile fabrics in Indonesia used in a province called Sulawesi Tengah. Bomba Textile has a unique pattern and has a philosophical meaning in human life in Sulawesi Tengah. Bomba Textile has many motif patterns and varied colors. The problem in this research is the difficulty in classifying every The Bomba textile motif in each class. Data classification is needed to recognize the motif of each Bomba textile pattern and to cluster it into the appropriate class. The features used to classify the Bomba textile motif is the textural feature. Texture features obtained from Gray-Level Co-occurrence matrices (GLCM) method consisting of energy, contrast, homogenity and correlation with four angles 0°, 45°, 90°, and 135°. This research will implement Quadratic Vector Machine (QSVM) method with texture feature on Bomba textile pattern. The use of a single of texture features by involving all features at all angles can improve the accuracy of the classification model. This research produces a model of motif classification on the Bomba textile which has the classification accuracy of 94.6% and error rate of 5.4%.