Implementasi Metode Extreme Learning Machine pada Klasifikasi Jenis Penyakit Hepatitis berdasarkan Faktor Gejala - Salsabila Multazam; Imam Cholissodin; Sigit Adinugroho (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2020)
Virus infection becomes very serious problem in medical world. Currently there are many viruses in Indonesia, including Human Immunodeficiency Virus (HIV), Novel Coronavirus (COVID-19), Dengue Virus (DENV), and Hepatitis A&B (HVA & HVB). It was recorded that in 2014 Hepaitits sufferers increased every year for the population aged above 15 years that is Hepatitis A (19.3%) and Hepatitis B (21.8%). To highly pay attention to the disease is very curcial considering Hepatitis sufferers often do not know already got infected by hepatitis. In this thesis the researchers is classifying Hepatitis types based on their symptoms using ELM method.The data being used is primary one gotten from the documents of patients infected by Hepatitis. There are 100 data with 20 features and 2 classes, namely Hepatitis A and Hepatitis B. This research was conducted in several stages from data normalization, followed by training process of the obtained data and then finally to verify the tested data input as well as data from the training process result. Based on the test results, the best ratio between train data and test data is 80: 20. This study uses several parameters to get optimal results including using 7 Hidden Neurons and the activation function used by Sigmoid Binary. By using these parameters obtained an average accuracy of 80.00%. It can be concluded that the use of the Extreme Learning Machine method can solve classification problems quite well.