Aplikasi Pengenalan Pola Batik Dengan Menggunakan Metode Gray-Level Cooccurrence Matrix
(1) Program Studi Teknik Informatika
(2) Program Studi Teknik Informatika
(3) Program Studi Teknik Informatika
(*) Corresponding Author
Abstract
Indonesia is country with rich culture. Since October 2nd 2009 batik has been officially recognized by UNESCO as Indonesia’s authentic cultural heritage. In addition to its unique patterns, batik has a deep philosophical significance. However there is no application that can introduce many kinds of batik to the society. This application is expected to address that issue.
This application uses gray-level cooccurrence matrix to extract texture features from an image of batik. The texture features extracted from a number of batik images create a dataset which can be used to create a decision tree. The decision made by the decision tree is the isen presented in batik image.
Test results of batik tulis recognition is maximum accuracy of 47.62%, which is considered low. The reason for that is supposedly the lack of texture patterns in batik tulis.
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Bhatt, H., Mehta, S., D‟mello, L.R. 2015. Use of ID3 Decision Tree Algorithm for Placement Prediction. International Journal of Computer Science and Information Technologies.
Hall-Beyer, Mryka. GLCM Texture Tutorial. http://www.fp.ucalgary.ca/mhallbey/tutorial.htm, diakses 25 November 2015
Intan, R., Yuliana, O.Y. 2009. Fuzzy Decision Tree Induction Approach for Mining Fuzzy Association Rules. ResearchGate.
Jiawei, H. 2001. Data Mining: Concepts and Techniques. San Fransisco.
Kemdikbud, Kamus Besar Bahasa Indonesia. http://kbbi.web.id/batik, diakses 25 November 2015
Nurhaida, Ida, et al., 2012. Performance Comparison Analysis Features Extraction Methods for Batik Recognition
Pathak, B., Barooah, D. 2013. Texture Analysis Based On the Gray-Level Co-occurrence Matrix Considering Possible Orientations, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering.
Pratiwi, M., Alexander, Harefa, J., Nanda, S. 2015. Mammograms Clasification using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network. 2015 International Conference on Computer Science and Computational Intelligence.
Quinlan, J.R. 1985. Induction of Decision Trees. Boston: Kluwer Academic Publishers.
Santoni, M.M., Sensuse, D.I., Arymurthy, A.M., Fanany, M.I. 2015. Cattle Race Classification Using Gray Level Co-occurrence Matrix Convolutional Neural Networks. 2015 International Conference on Computer Science and Computational Intelligence.
Sun, F., Liu, Y., Xurigan, S., Zhang, Q. 2015. Research of Clothing Sales Prediction Analysis Based on ID3 Decision Tree Algorithm. International Symposium on Computers & Informatics.
UNESCO. Fourth Session of the Intergovernmental Committee (4.COM). http://www.unesco.org/culture/ ich/en/RL/indonesian-batik-00170, diakses 25 November 2015
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