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Implementation of Random Forest for Motif Classification Based on Sift Ahkyar Khadafi; Muhammad Iqbal
Jurnal Mantik Vol. 5 No. 4 (2022): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Songket an example of intangible cultural heritage found in Indonesia. Songket, especially Songket Palembang, has a lot of variety depending on the features of each place. When compared to Songket from other locations, Palembang Songket has more features. Songket Palembang has a high motive, quality, and complexity in the manufacturing process, in addition to its historical significance. Using Scale-Invariant Feature Transform (SIFT) feature extraction, the Random Forest approach was employed to identify the Songket Palembang motif image in this study. The SIFT approach involves the steps of extrema detection scale space, keypoint localization, orientation assignment, and keypoint descriptor in the feature creation process. The Random Forest categorization uses the resulting feature. In this study, 115 photographs of each sort of Songket theme were used, including Chinese Flowers, Beautiful Flowers, and Pulir. Each Songket Palembang motif's five colors were used to create the image. For each Songket Palembang motif, 100 and 15 training and test data were used, respectively. The test results show that the SIFT and Random Forest methods for the classification of Songket Palembang motifs can provide fairly good accuracy, with overall accuracy of 92.98 percent, per class accuracy of 94.07 percent, precision 92.98 percent, and recall 89.74 percent for the SIFT and Random Forest methods, respectively.