Zalti, Shaza Nashwa (2025) Klasifikasi Citra Motif Batik Kuantan Singingi Menggunakan Metode Local Binary Patterns (lbp). Other thesis, Universitas Islam Riau.

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Abstract

Kuantan Singingi batik is an important part of Indonesia's cultural heritage that has deep historical and symbolic values. However, there are still limitations in an automated system that can identify and classify these various batik motifs, leading to a lack of understanding of the variety of motifs that exist. Therefore, the implementation of this system has great potential to introduce Kuantan Singingi batik motifs to the public and tourists more widely, as well as support cultural preservation efforts through technology. In addition, the system can also be a source of reference for researchers and batik industry players in analyzing the patterns and unique characteristics of each motif. This research aims to develop an automatic classification system for Kuantan Singingi batik motifs by utilizing the Local Binary Patterns (LBP) method followed by classification using the K-Nearest Neighbor (KNN) algorithm. Through this approach, it is expected that the system can accurately recognize batik texture patterns as well as provide more structured information about the variety of motifs available. The results showed that the combination of Local Binary Patterns (LBP) and K-Nearest Neighbor (KNN) methods successfully classified five types of Kuantan Singingi batik motifs with an accuracy rate of 79%.

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