Pengenalan Pola pada Batik Lontara berbasis Kecerdasan Buatan - Mohammad Yazdi Pusadan; Fuad Mahfud; Anisa Yulandari; Sabarudin Saputra (Prosiding SISFOTEK, 2025)
Batik is an Indonesian cultural heritage in which almost every region has its own distinctive batik with diverse motifs. UNESCO designated batik as a world cultural heritage created by the Indonesian people in 2009. In South Sulawesi, there is also batik called Batik Lontara. Batik Lontara itself is a type of Bugis-Makassar batik unique to South Sulawesi that features motifs of the Lontara script. The purpose of this research is to implement the extraction of woven Batik Lontara and stamped Batik Lontara using the GLCM (Gray Level Co-occurrence Matrix) method and the KNN (K-Nearest Neighbor) algorithm to recognize the types of Batik Lontara. The Gray Level Co-occurrence Matrix (GLCM) is a feature extraction method that uses second-order texture calculations, considering pairs of two pixels from the original image. This research employs the K-Nearest Neighbor (KNN) algorithm, which is a method for classifying objects based on training data with the closest distance to the test data. The research material used is images of Batik Lontara with various motifs, namely woven Batik Lontara and non-woven Batik Lontara. Based on the Batik Lontara images, a process of converting the images from RGB to Grayscale will be carried out. The expected output of this research is a reputable international journal publication.