A Survey: Effect of the Number of GLCM Features on Classification Accuracy of Lasem Batik Images using K-Nearest Neighbor

Abstract

Lasem batik is one of traditional Indonesian’s famous batik that has high artistic and economic value. Lasem batik can be classified into several different motifs. This study aims to analyze the number of gray level co-occurrence matrix (GLCM) features during the process of classification of Lasem batik image with K-Nearest Neighbor (KNN). KNN has advantages in overcoming probability density, able to consolidate calculations based on the number of neighbors specified, and can perform calculations with limited parameters. Feature extraction is one of the important steps before performing image classification. GLCM is one of the extraction features of a very popular texture. There are five GLCM features that are widely used, namely contrast, homogeneity, energy, correlation, and entropy. This research classifies five kinds of the famous motif of batik Lasem. The training and testing process compare the use of four and five GLCM features for each of the three experiments with different amounts of data. The test results show that the use of four and five types of GLCM features get the same accuracy in each experiment. It can be concluded that with KNN enough to use four kinds of features to speed up the calculation of the classification.