KOMPARASI PERFORMA ALGORITMA K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI CITRA TEKSTUR TENUN - budiman baso (Jurnal Komputer dan Teknologi, 2026)
The diversity of woven fabrics on Timor Island makes it difficult to distinguish between types of woven fabrics and their origins. Each region on Timor Island has its own woven fabric motifs that represent local culture. There are Timor woven motifs that look similar but have different types. Each motif and process of making weaving on Timor Island can describe the type and origin of the weaving. To distinguish Timor woven fabrics, it can be seen from the style of motifs or textures contained in Timor woven fabrics. Therefore, the pattern recognition of Timor woven motifs with the concept of classification is implemented using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classification algorithms based on texture feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). This study aims to compare the performance of the two classification algorithms. Several parameters are used to configure the KNN and SVM algorithms to determine the performance gap between the two algorithms. The experimental architecture was carried out on 500 images of Timor weaving with 4 motifs; the image dataset will be divided into 75% training data and 25% testing data using the hold-out validation method. From the experimental results, the KNN algorithm using Euclidean distance with 1 Neighbor obtained the best performance, with an Accuracy rate of 88.73%, Precision of 89.41%, Recall of 88.73%, and F1-Score of 89.07%.