Implementasi Random Forest Untuk Klasifikasi Motif Songket Palembang Berdasarkan SIFT - Siska Devella; Yohannes Yohannes; Firda Novia Rahmawati (JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 2020)
Indonesia has a variety of intangible cultural heritage, one of which is songket. Songket has a lot of variety according to the characteristics of each region, especially Songket Palembang. Songket Palembang has more features compared to songket from other regions. Besides having historical value, Songket Palembang has a high motive, quality, and complexity in the manufacturing process. In this study, the Random Forest method was used to classify the Songket Palembang motif image by using Scale-Invariant Feature Transform (SIFT) feature extraction. The process of feature formation using the SIFT method is through the stages of extrema detection scale space, keypoint localization, orientation assignment, and keypoint descriptor. The resulting feature is used for the Random Forest classification. Songket motif images used in this study were 115 images of each type of motif, namely Chinese Flowers, Beautiful Flowers, and Pulir. Image selection is taken from 5 colors of each Songket Palembang motif. Training data and test data used were 100 and 15 for each Songket Palembang motif, respectively. The test results show that the SIFT and Random Forest methods for the classification of Songket Palembang motifs can provide a pretty good accuracy, where the SIFT and Random Forest methods can produce an overall accuracy of 92.98%, per class accuracy of 94.07%, precision 92.98%, and recall 89.74%.