Determination of SVM-RBF Kernel Space Parameter to Optimize Accuracy Value of Indonesian Batik Images Classification
Avinanta Tarigan, Dewi Agushinta, Adang Suhendra, Fikri Budiman · Journal of Computer Science · 2017
Image retrieval using Support Vector Machine (SVM) classification very depends on kernel function and parameter.Kernel function used by dot product substitution from old dimension feature to new dimension depends on image dataset condition.In this research, parameter of Gaussian /Radial Basis Function (RBF) kernel function is optimized using multi class non-linear SVM method and implemented to training and test datasets of traditional Indonesian batik images.The batik images dataset is limited to four geometric motifs textures, which are ceplok/ceplokan, kawung, nitik and parang/lerang.Discrete Wavelet Transform level 3 daubechies 2 is used to result feature dataset of traditional batik images dataset of four classes geometric motifs textures.The batik images are used for training and test dataset in SVM-RBF kernel parameter optimation to maximize accuracy value in non-linear multi-class classification.Cross Validation and Grid-search methods are used to analyze and evaluate SVM-RBF kernel parameter optimation.Confusion matrix measurement method is used to result accuracy value in every evaluation conducted in every combination of cost function/C and gamma/γ as SVM-RBF kernel parameter.Maximum accuracy parameter value is C = 2 7 and γ = 2 -15 achieved by 10 times evaluation wit different test dataset for each evaluation.Maximum accuracy value is 0.77 to 0.86.