Batik Image Classification Using Treeval and Treefit as Decision Tree Function in Optimizing Content Based Batik Image Retrieval

This research is to increase the percentage of similarity and to increase the speed of the retrieval of characteristic of batik image which is the texture and shape. In order to obtain an optimal result, the classification process is performed using a decision tree with treeval and treefit function, where the value used is the result of the image feature extraction. For this image extraction, the values that originate from the approximation coefficient that uses the wavelet transform method deubecheuss level 2 and invariant movement. The research is performed on 7 types of pattern and 225 images. The result using 5 types of batik patterns namely lereng, parang, kawung, nitik and truntum using 20 test data on each pattern, has a similarity percentage above 80 - 85 percent. For 2 other patterns which is mega mendung and ceplok using 10 data on each pattern, has a similarity percentage above 30 – 40 percent only. Based on the result, further research is required to using other methods and functions.