Implementasi Extreme Learning Machine Untuk Deteksi Dini Infeksi Menular Seks (IMS) Pada Puskesmas Dinoyo Kota Malang - Fikhi Nugroho; Imam Cholissodin; Suprapto Suprapto (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Sexually Transmitted Infections (STI) is a major public health problem in the world. Incidence of STI cases in many developing countries such as failure in diagnosing and provide treatment at an early stage can lead to serious complications. The required input parameters consist of 39 features consisting of 2 sexes, 9 risk factors, and 29 symptoms. The process of identifying early identification of STI symptoms in this case will implement Extreme Learning Machine (ELM). The implementation of ELM itself does not require IMS rules related to the exact rules but rather compares the results of both determinations. Thus, if there is a change of calculation or identification provisions, it does not affect the calculation of ELM. The ELM method is used to determine STI disease to a number of 17 classes. The best results of the three test scenarios of accuracy between ELM calculations and expert diagnosis results were 36,36% for the 90:10 ratio, 50% for 100 hidden layers, and 31.82% for the weight range of -1 to 0.