MATEC Web Conf.
Volume 280, 2019The 5th International Conference on Sustainable Built Environment (ICSBE 2018)
|Number of page(s)||10|
|Section||Sustainable Resource Management|
|Published online||08 May 2019|
Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)
Information Technology, Faculty of Engineering, Lambung Mangkurat University, Indonesia
* Corresponding author: firstname.lastname@example.org
Sasirangan is one of the traditional cloth from Indonesia. Specifically, it comes from South Borneo. It has many variations of motifs with a different meaning for each pattern. This paper proposes a prototype of Sasirangan motifs classification using four (4) type of Sasirangan motifs namely Hiris Gagatas, Gigi Haruan, Kulat Kurikit, and Hiris Pudak. We used primary data of Sasirangan images collected from Kampung Sasirangan, Banjarmasin, South Kalimantan. After that, the images are processed using Scale-Invariant Feature Transform (SIFT) to extract its features. Furthermore, the extracted features vectors obtained is classified using the Support Vector Machine (SVM). The result shows that the Scale- Invariant Feature Transform (SIFT) feature extraction with Support Vector Machine (SVM) classification able to classify Sasirangan motifs with an overall accuracy of 95%.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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