Klasifikasi Citra Batik Menggunakan Local Binary Pattern (LBP) dan Support Vector Machine (SVM) - Fatimah, Nuris Sayyidatul; Agustin, Soffiana (Jurnal Algoritma, 2025)
Image classification is one of the important branches in digital image processing that aims to recognize and classify objects based on certain features. This research seeks to preserve Indonesian batik culture through digital documents of batik motifs that can be applied in museums and research institutions. The main objective of this research is to overcome the difficulty in classifying batik motifs by applying Local Binary Pattern (LBP) as a feature extraction technique and Support Vector Machine (SVM) as a classification algorithm. The batik motifs used are Batik Kawung, Batik Megamendung, and Batik Parang. By using 720 batik images. This research was conducted in four main stages, namely pre-processing, feature extraction, classification and evaluation. The results showed an accuracy of 88.89%, with varying precision, recall, and F1-Score. The results show that texture analysis extracted through LBP contributes significantly to the accuracy of batik motif recognition.