Abstract
In this study, batik has been modeled using the GLCM method which will produce features of energy, contrast, correlation, homogenity and entropy. Then these features are used as input for the classification process of training data and data testing using the K-NN method by using ecludean distance search. The next classification uses 5 features that provide information on energy values, contrast, correlation, homogeneity, and entropy. Of the two classifications, which comparison will produce the best accuracy. Training data and data testing were tested using the Recognition Rate calculation for system evaluation. The results of the study produced 66% recognition rate in 50 pieces of test data and 100 pieces of training data.
Highlights
The development of the era influenced the development of batik, batik was originally done by drawing using canting, the result of which we are familiar with hand-made batik, but in modern times modern batik appears by combining hand-painted batik and printed batik, or printed batik [1]
Another study was conducted by Sutojo et al [7], regarding classification of cattle types based on texture in cattle images with output of 5 features of Gray Level Co-Occurrence Matrix (GLCM) namely contrast, homogeneity, correlation, entropy and energy which produced an average accuracy of 95%
2.2 K-Nearest Neighbor (K-NN) K-NN is a method for classifying objects based on learning data which is the closest distance to the object [6]
Summary
The development of the era influenced the development of batik, batik was originally done by drawing using canting, the result of which we are familiar with hand-made batik, but in modern times modern batik appears by combining hand-painted batik and printed batik, or printed batik [1]. Another study was conducted by Sutojo et al [7], regarding classification of cattle types based on texture in cattle images with output of 5 features of GLCM namely contrast, homogeneity, correlation, entropy and energy which produced an average accuracy of 95%. From these studies, it can be seen that the level of classification accuracy is influenced by the amount of data, feature extraction, and the method used. Another research by [9], has been conducted by grayscale processing, binary and canny processes and the result of invariant moment’s calculation and has been combinated using Canny detection to enhance result during Wavelet Transform implementation
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