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Journal from gdlhub / 2018-07-25 15:57:28
Oleh : Agus Eko Minarno, Ayu Septya Maulani, Arrie Kurniawardhani, Fitri Bimantoro, Nanik Suciati, Telkomnika
Dibuat : 2018-07-25, dengan 1 file

Keyword : Batik, Classification, Multi Texton Histogram, k-Nearest Neighbor, Support Vector Machine. Full Text: PDF DOI: http://dx.doi.org/10.12928/telkomnika.v16i0.7376 Refbacks There are currently no refbacks. Copyright (c) 2018 Universitas Ahmad Dahlan Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. TELKOMNIKA Telecommunication, Computing, Electronics and Control website: http://telkomnika.ee.uad.ac.id online system: http://journal.uad.ac.id/index.php/TELKOMNIKA Phone: +62 (274) 563515, 511830, 379418, 371120 ext: 3208 Fax : +62 (274) 564604 StatCounter - Free Web Tracker and Counter View TELKOMNIKA Stats
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/7376
Sumber pengambilan dokumen : WEB

Batik is a symbol reflecting Indonesian culture which has been acknowledged by UNESCO since 2009. Batik has various motifs or patterns. Because most regions in Indonesia have their own characteristic of batik motifs, people find difficulties to recognize the variety of Batik. This study attempts to develop a system that can help people to classify Batik motifs using Multi Texton Histogram (MTH) for feature extraction. Meanwhile, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithm will be employed for classification. The performance of those classifications is then compared to seek the best classification method for Batik classification. The performance is tested in 300 images divided into 50 classes. The results show the optimum accuracy achieved using k-NN with k=5 and MTH with 6 textons is 82%; however, SVM and MTH with 6 textons denote 76%. According to the result, MTH as feature extraction, k-NN or SVM as a classifier can be applied on Batik image classification.

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