CONTENT BASED BATIK IMAGE CLASSIFICATION USING WAVELET TRANSFORM AND FUZZY NEURAL NETWORK

Abstract

In this paper we introduce the content-based image classification using wavelet transform with Daubechies type 2 level 2 to process the characteristic texture consisting of standard deviation, mean and energy as Input variables, using the method of Fuzzy Neural Network (FNN). All the input value will be processed using fuzzyfication with 5 categories namely Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be fuzzy input in the process of classification with neural network method. Batik images will be processed using 7 (seven) types of batik motif which is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process using FNN is Rule generation, such that for a new image of batik motif types can be immediately determined after FNN classification is completed. For the level of precision, this method is between 90-92%, including if we use the rule generation to determine the level precision is between 90-92%.