Klasifikasi Gangguan Jiwa Skizofrenia Menggunakan Algoritme Support Vector Machine (SVM) - Daisy Kurniawaty; Imam Cholissodin; Putra Pandu Adikara (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Insanity is the most common disease. One of insanity is schizophrenia. The process of diagnosis of schizophrenia is difficult, because there is no specific characteristic of behavior or appearance for the sufferer, some sufferer can behave and look like normal people and expensive treatment. It will make the patient's condition worse. To resolve this issue, this can be done with schizophrenia classification using support vector machine (SVM) algorithm. In this study there are 75 data that is divided into two types of schizophrenia, that is paranoid and simplex. The method in this study using support vector machine algorithm, wich to the category of good classification method, provides a statistical approach in pattern recognition, and is a linear method, but SVM provides kernel trick, which can solve problems related to non-linear classification. The result obtained using SVM 100% accuracy using ratio data 90%:10%, gamma = 0,00001, lambda = 3, C = 0,01, kernel polynomial of degree, maximum iteration is 1000.