<![CDATA[This study develops an automatic classification system for Timor weaving motifs using the Local Binary Pattern (LBP) method for feature extraction and Support Vector Machine (SVM) for classification. The study aims to facilitate and accelerate the identification of traditional Timor weaving motifs through the application of machine learning. The dataset consists of 1,500 images obtained through direct image acquisition and documentation of six Timor weaving motifs: Nunkolo, Futus Pelangi, Kemak, Kaimafafa, Mabuna, and Biboki. The research process includes image preprocessing, feature extraction using LBP with 8 sampling points and a radius of 1 pixel, and classification using multi-class SVM with the One-vs-One (OVO) strategy. For the six classes, the OVO strategy produces 15 binary classifiers evaluated using three kernels: Linear, Polynomial, and Radial Basis Function (RBF). The SVM parameters used are C = 1 and degree = 3. The evaluation results show that the Linear kernel provides the best performance, with an accuracy of 90.66%, precision of 90.96%, recall of 90.66%, and F1-score of 90.81%. The Polynomial and RBF kernels produce lower performance. These results indicate that the combination of LBP and OVO-SVM with the Linear kernel is effective for classifying six Timor weaving motifs and can serve as an image-based tool to assist in motif identification]]>
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