Dewanto Harjunowibowo, Sri Hartati, Rosihan Ariyuana, Aris Budianto
Publication year: 2012

This research is aimed to test the originality of paper currency using detection system based on Linear Vector X1 Quantization (LVQ) Neural Network method. The input image of the system is the “dancer” object image of paper currency Rp. 50.000,- fluorescent by ultraviolet light. The coding was carried out using visual programming language. The feature’s size of the dancer tested object is 114×90 px and the Red-Green-Blue-Hue- X2 Saturation-Intensity (RGBHSI) values were extracted as the input for LVQ. The experimental result shows that the system has an accuracy 100% of detecting 20 real test case data, and 96% of detecting 22 simulated test case data.