Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network


  • A Haris Rangkuti Bina Nusantara University




batik image, wavelet transform, daubechies, Fuzzy neural network, fuzzifikasi, rule generation, batik motif



This paper introduces a classification of the image of the batik process, which is based on the similarity of the characteristics, by combining the method of wavelet transform Daubechies type 2 level 2, to process the characteristic texture consisting of standard deviation, mean and energy as input variables, using the method of Fuzzy Neural Network (FNN). Fuzzyfikasi process will be carried out all input values with five categories: Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be a fuzzy input in the process of neural network classification methods. The result will be a fuzzy input in the process of neural network classification methods. For the image to be processed seven types of batik motif is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process with FNN is rule generation, so for the new image of batik can be immediately known motif types after treatment with FNN classification.  For the degree of precision of this method is 86-92%.

Plum Analytics


Ajay, K.S., Tiwari, S., Shukla, V. P. (2012). Wavelet Base Multi Class Image Classification using Neural Network. International journal of Computer Application, 37(4)

Balamurugan, V., Anandhakumar, P. (2009). Neuro-Fuzzy Based Clustering Approch for Content Based Image Retrieval Using 2D-Wavelet Transforn. International Journal of Recent Trend in Engineering, 1(1).

Franca, C. A., Gonzaga, A. (2010). Classification of wood plates by neural networks and Fuzzy logic. University Federal at Sao Carlos.

Hazra, D. (2011). Texture Recognition with combined GLCM, Wavelet and Rotated Wavelet Features. International Journal of Computer and Electrical Engineering, 3(1).

Kokare, M., Biswas, P. K., Chatterji, B. N. (2007), Texture image retrieval using rotated wavelet filters. Pattern Recogn. Lett., 28, 1240-1249.

Kulkarni, A. D. (2001). Computer Vision and Fuzzy Neural System. Upper Saddle River, NJ: Prentice HALL.

Kulkarni, A. D., MC Caslin, S. (2006). Fuzzy Neural Network Models For Multispectral Image Analysis. USA: Circuit System, Electronics control, Signal Processing.

Moertini, V. S., Sitohang, B. (2005). Algorithms of Clustering and Classifying Batik Images Based on Color, Contrast and Motif. PROC. ITB Eng. Science, 37 B(2), 141-160.

Mojsilovic A., Popovic M.V., Rackov, D. M. On the selection of an optimal wavelet basis for texture characterization. IEEE Transactions on Image Processing, 9, 2043–2050.

Park S.B., Lee, J. W., Kim, S. K. (2003). Content-based image classification using a neural network. Pattern Recognation, 287-300,

Pahludi, P. N. (2006). Klasifikasi Citra Berdasarkan Tekstur Mengunakan jaringan Saraf Tiruan Peramabatan Terbalik.

Pratikaningtyas, D. et al. (2010). Klasifikasi batik Mengunakan Metode Transformasi Wavelet. Paper Skripsi, UNDIP.

Rahadianti, L., Manurung, R., Murni, A. (2010). Clustering Batik Images based on Log-Gabor and Colour Histogram Features. University of Indonesia.

Rangkuti, A. H., Bahaweres, R. B., Harjoko, A. (2012). Batik image retrieval base on similarity of shape and texture characteristic. International Conference on Advanced Computer Science and Information Systems (ICACSIS 2012), Universty of Indonesia

Sanabila, H. R., Manurung, R. (2009). Recognition of Batik Motifs using the Generalized Hough Transform. University of Indonesia.

Sopika, J., Jason, G. (2007). Neural Network and Fuzzy Logic Approch for Satelite Image Classification: A Review.

Wahyudi, Azurat, A., Manurung, M., Murni, A. (2009). Batik Image Reconstruction Based On Codebook and Keyblock Framework. University of Indonesia.

Zhu, L., Zhang, A. (2002). Theory of Keyblock-based Image Retrieval. ACM Journal, 1-32.






Abstract 1118  .
PDF downloaded 762  .