Rupiah Classification System using Segmented Fractal Texture Analysis and HSV Color Features

The crime of forgery of rupiah currency can be anticipated by examining the rupiah banknotes based on traits or features contained on the original paper money. Features that are not owned by the rupiah banknote counterfeit is an ultraviolet sign that are owned by the original paper money. Rupiah banknotes feature extraction consists of a combination of color and texture feature extraction. The proposed method is the HSV color histogram for color feature extraction and Segmented Fractal Texture Analysis (SFTA) for texture feature extraction. The combination of HSV and SFTA is expected to improve the performance of rupiah banknotes feature extraction. Moreover this paper will analyze feature redundancy in Two Threshold Decomposition Algorithm in SFTA Algorithm. Experimental results show the proposed method can reach 100% accuracy. Experiment results also show that redundant features can be removed without affecting the accuracy of of the system so that it can reduce the computational cost.