Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction

Abstract

One of the arts in Surakarta culture is batik cloth. A batik is a form of heritage from the nation's ancestors whose manufacturing process must use specific tools and materials. Surakarta's typical batik has many patterns and motifs, such as Sawat, Satriomanah, and Semenrante. The pattern is a picture framework whose results will display the type of batik. A batik may resemble one type and another, so a classification technique is needed to determine the type of batik. This study aims to develop a classification method for batik cloth using the Naïve Bayes classification technique. The feature extraction used is the Gray Level Co-Occurrence Matrix (GLCM) to obtain texture values in each image. The stages in this research include pre-processing, feature extraction, classification, and testing. The training data in this study were 200 images for each Sawat, Satriomanah, and Sementrante class obtained from the data augmentation method by flipping, zooming, cropping, shifting, and changing the brightness of the images. The total sample data is 600 images. The amount of training data and data testing was divided three times (60% training and 40% testing), (70% training and 30% testing), and (80% training and 20% testing) for accuracy. In this study, the Naïve Bayes method using WEKA 3.8.6 tools obtained the best accuracy of 97.22% using a 70% percentage split compared to using 80% and 60% percentage splits with a result of 96.66%, this difference occurs due to differences in training data and test data. The results of this study indicate that the Naïve Bayes method can be used to classify batik cloth patterns based on texture feature extraction.

Synthetic Aperture Radar Remote Sensing for Crop Classification

“…In agriculture sector it is used to monitor health of crops, estimate yield, and classify crops based on land cover and monitor water stress [1]. However, among all the applications of remote sensing, crop classification is a challenging task because of the similarity in texture and colour of crops in their initial stages [17][18][19][20][21]. High resolution images have advanced discrimination capabilities, but obtaining time series datasets that cover the entire growth cycles of crops is challenging [3][4][5][6][7].…”