KLASIFIKASI JENIS BATIK MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS (KNN)
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Abstract
Batik is a world heritage that has been recognized by UNESCO, has high philosophical and artistic value and economic value. However, with so many types of batik in Indonesia, many people still have difficulty knowing and distinguishing between these types of batik, which can threaten its sustainability. Lack of public knowledge about the types of batik can lead to reduced appreciation and loss of cultural identity. Therefore, appropriate treatment is needed to overcome this problem. This research aims to build a classification system for types of batik using the K-Nearest Neighbors (KNN) algorithm to help the public recognize and differentiate types of batik, especially Batik Lasem, Batik Sekar Jagad, Batik Tambal, and Batik Truntum. It is hoped that this system can increase public awareness and knowledge about the richness of batik culture and help in preserving Indonesia's cultural heritage. The system testing results show differences in accuracy in each experiment with a dataset of 200 images, consisting of 50 images per type of batik. The highest accuracy was obtained from the third experiment, namely 85%, while the second experiment resulted in an accuracy of 72.5%, and the first experiment obtained an accuracy of 65%
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