A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor - Cahaya Jatmoko; Daurat Sinaga (Universitas Dian Nuswantoro and IndoCEISS, 2018)
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.