EFEK PENERAPAN MODEL NORMALISASI DATA LUARAN OLFAKTORI ELEKTRONIK (E-NOSE) PADA TINGKAT PENGENALAN POLA - , FRIDA AGUNG RAKHMADI ([Yogyakarta] : Universitas Gadjah Mada, 2011)
The pre-processing using seven normalization models was carried out to improve the degree of pattern discrimination of electronic nose (e-nose) outputs which are initially similar each other. All models are adopted from the well known models that are usually used in data processing. They are: range scale1 , range scale2, relative scale1, relative scale2, baseline subtraction, global method, and local method. In this case, high discrimination degree is important in improving the performance of pattern recognition system of e-nose. To provide a set of trained samples, six variants of mix ratio between turmuric and wild ginger were exposed to e-nose. Before being trained to the pattern recognition system, all the patterns were normalized. The pattern recognition system used in this research is back propagation artificial neural network (BP-ANN). Effect of the seven models on the performance of pattern recognition system is marked by the percentage of recognition repeatibility in recognizing the patterns of test samples. In this case, the test samples were treated using the same procedures similar to that of the trained samples. As the result, the normalization effect may improves the pattern discrimination visually. However, from the seven normalization models, range scale1 and range scale2 are the best models shown by the high (90%) pattern recognition degree and repeatibility.