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Silvia Aprilla
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Penyakit Skizofrenia dan Episode Depresi Pada Gangguan Kejiwaan Dengan Menggunakan Metode Support Vector Machine (SVM) Silvia Aprilla; Muhammad Tanzil Furqon; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Psychiatric disorders are disorders of the human brain that is not normal or different from people in general. There are many types of psychiatric disorders. Schizophrenia and Depression are a type of psychiatric disorders suffered by many people. There are also types of Schizophrenia and Depression, one type of disease in each is Schizophrenia Hebephrenic and Psychotic Depression. According to data in the soul hospital of Dr. Radjiman Wediodiningrat Lawang, both of these diseases are included in the top 10 diagnoses of outpatient and outpatient illnesses in 2017 which reached over 22.000 people. Due to a large number of patients affected by the disease, soul hospital needed a system that can classify Schizophrenia Hebephrenic and Psychotic Depression Disease. Classification is the manufacture of a model that used to make a group for an object with the same characteristics into a determined class. To classify the disease used support vector machine (SVM) algorithm with the polynomial of degree 2 kernel. The data used are 200 data taken from soul hospital of Dr. Radjiman Wediodiningrat Lawang. This data consists of 80% data training and 20% data testing. The test method used is K-fold cross-validation. Based on the results of testing SVM parameters obtained the highest average accuracy is 79% with the value of γ = 0,00001, λ = 0,1, C = 0,01, max iteration = 150, and ɛ = 1.10-10.
Co-Authors M. Ali Fauzi Muhammad Tanzil Furqon