Implementasi Algoritme Support Vector Machine (SVM) Untuk Klasifikasi Penyakit Dengan Gejala Demam - Nurul Ihsani Fadilah; Bayu Rahayudi; Muhammad Tanzil Furqon (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Infectious disease in humans have one of the general indications, that is Fever. There are three diseases with symptoms of fever that transmission of disease occurs by the media Arthropod-borne disease, such as dengue fever, malaria, and typhoid. The disease has almost the same clinical symptoms, that is difficult to make a diagnosis of the disease suffered by the patient. Because of a large number of patient and a high risk of death in this disease, need a system that can distinguish these three diseases quickly and precisely. To solve the problem, the system is needed to classify the disease with fever symptoms using the Support Vector Machine (SVM) algorithm. This research uses 130 datasets that have 15 parameters. The dataset is divided into train data and test data by using K-Fold Cross Validation method, with k=10. The final result from SVM algorithm implementation for disease classification with symptoms of fever is the accuracy of the system capabilities in classifying dengue fever class, malaria class, and typhoid class. So, the best average value of accuracy in this implementation is 99.23%, using k-fold cross validation, with k=10, division of data ratio=90%:10%, and the parameters used are lamda=0.5, gamma=0.01, C(Complexity)=1, epsilon=0.0001, maximum iteration=20.