PERBANDINGAN DEFUZZIFIKASI CENTROID DAN MAXIMUM DEFUZZIFIER PADA METODE FUZZY INFERENCE SYSTEM UNTUK DIAGNOSIS PENYAKIT JANTUNG - Satia Suhada; Dwiza Riana (Universitas Bina Sarana Informatika Kota Sukabumi, 2016)
In the past decade, heart disease was ranked first of the highest cause of death in the world. Asrevealed by the World Health Organization (WHO), as many as 7.4 million people died every year dueto this disease. Application of fuzzy logic is often used for several reasons, among them because ofthe underlying mathematical concepts of fuzzy reasoning is very simple and easy to understand, veryflexible, has a tolerance of the data inappropriately. Basic fuzzy logic that is fuzzy set theory, in whichthe role of determining the existence of degrees of membership as elements in a set is absolutelyessential. In addition the use of fuzzy logic in the current medical diagnosis is quite inflated. The mainprocesses in the Mamdani fuzzy logic starts from the input whose value was changed into fuzzy sets,fuzzy inference, then defuzzification to return the fuzzy set into crisp output. The process ofdefuzzification has an important role, because different defuzzification methods will produce the setfirmly. In this study compared defuzzifikasi centroid with maximum defuzzifier model mean of maxima.The results for diagnosis using the defuzzifikasi centroid has the accuracy rate of 90.3% and fordefuzzifikasi mean of Maxima results of diagnosis is only 86,5%.