Identifikasi Varietas Koi Berdasarkan Gambar Menggunakan Zero Parameter Simple Linear Iterative Clustering dan Support Vector Machine

Amadea Sapphira, Alexander Setiawan, Endang Setyati

Abstract


There’s currently 120 types of koi fish that has been bred around the world. The types of koi fish depends on the color patterns and shapes they have. There’s alot of patterns that has similarity between one type with another. For example, sanke and showa koi fish will look similar from a non-expert’s point of view, because both type has same color pattern, which is red, black and white. In actuality, sanke koi is dominantly red and white with slight black accent, while showa’s dominant color is red and black, with white accent.

In this research, Zero Parameter Simple Linear Iterative Clustering (SLICO) method and Simple Linear Iterative Clustering (SLIC) will be tested and used to process the image segmentation process to eliminate the background of the image. Color Local Binary Pattern method is used to get the textures on images through the RGB, HSV, and grayscale colorspace. Support Vector Machine is used to identify types of koi fish. To test the SVM, two kind of kernel is used, which is linear kernel and Radial Basis Function (RBF) kernel.

The results of this study are the program able to recognize types of koi from iamges. The test results show an accuracy of 36% in grayscale colorspace, 50% in RGB colorspace, and 48% in HSV colorspace.


Keywords


Color Local Binary Pattern ; Support Vector Machine ; Zero Parameter Simple Linear Iterative Clustering

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References


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