Klasifikasi Batik dengan Ekstraksi Fitur Tekstur Local Binary Pattern dan Metode K-Nearest Neighbor - Muhammad Tegar Kanugroho; Muh. Arif Rahman; Randy Cahya Wihandika (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2022)
Indonesia is a country that consists of many islands and has a diversity of tribes that are scattered throughout its archipelago. Diverse ethnicities, various characteristics are also owned in order to distinguish one tribe from another. One of the distinguishing characteristics is batik, which is widely known and has become a cultural heritage. When viewed from the picture, the batik pattern has a texture. In digital image processing, texture can be used as an element that differentiates batik from one another, one of which is the Local Binary Pattern (LBP) method. By using the Local Binary Pattern (LBP) method, the texture of batik will be recognized as a feature of digital image processing, the batik image can be processed to obtain several similar images. The research process on batik begins with pre-processing, then extraction of texture features in the image using the Local Binary Pattern (LBP) method and continues with classification by K-Nearest Neighbor (KNN). In this study was using the normalized LBP value. At normalized values, the best results are using K-Nearest Neighbor with neighbors (K) = 5 by getting an accuracy of 65%