Optimasi Bobot Awal Extreme Learning Machine menggunakan Algoritme Genetika untuk Klasifikasi Penanganan Human Papilloma Virus - Rizki Ramadhan; Wayan Firdaus Mahmudy (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Human Papilloma Virus is a virus that causes warts in humans. There are so many types of treatment for this virus, the most common types of treatment are immunotherapy and cryotherapy. The symptoms that appear in the patient are almost similar, so that proper handling is needed. Based on this, the Extreme Learning Machine method is used to classify the types of treatment of Human Papilloma Virus. The symptom parameters used were 6 parameters and the classes used were immunotherapy and cryotherapy. In this research, the initial weight of the Extreme Learning Machine was optimized by Genetic Algorithm and then the weight was used by the Extreme Learning Machine method for the classification process of the types of treatment of the Human Papilloma Virus. The amount of data used is 118 data with the data ratio for the training process and the test process is 80:20. The Extreme Learning Machine parameters used are 10 hidden neurons and binary sigmoid activation functions. The test results obtained the best classification accuracy level of 100% for both treatment, cryotherapy and immunotherapy from 3 of 10 testing with an average computation time of 350,3 seconds using the initial weight which was optimized by the Genetic Algorithm with the best parameter population size of 70, the number generations of 160, the crossover rate of 0.9 and the mutation rate of 0.1.