Pre-Processing Of Resize And Region Of Interest (ROI) To Improve The Accuracy Of Batik Detection Based On VGG-16

The patterns and designs used in batik are a traditional textile art form from Indonesia. With the increasing popularity of computer vision applications, the interest in automatic detection and recognition of batik patterns is increasing. Convolutional Neural Networks (CNNs) show very good performance in detecting objects. The problem addressed in this study is the variation in image sizes, which affects accuracy. The selection of pre-processing techniques will impact the accuracy of the results. For this reason, it is important in this study to compare tests on data that has been processed using resize and Region of Interest (ROI). This study aims to determine the impact of two pre-processing techniques, resize and ROI, on the accuracy of batik pattern recognition using the Visual Geometry Group (VGG)-16 model. The dataset consisted of 1,445 images, with 1,301 images used for training and 144 images used for testing. The classes used were Kawung, Parang, Satriomanah, Sawat, Sementrante, Sidomukti, Tambal, and Truntum. The experimental results demonstrate that the choice of pre-processing techniques significantly affects the accuracy of batik pattern detection. Resizing provides an efficient computational solution, while ROI achieves a detection accuracy of 0.96, which is superior to Resize's accuracy of 0.89. This study highlights the importance of preprocessing techniques in detecting batik patterns using the VGG-16 model. Resizing and ROI have advantages and disadvantages, ROI techniques generally result in higher accuracy.