Penentuan Error Dalam Peramalan Jumlah Korban Demam Berdarah Dengue Menggunakan Metode Neural Network (Kasus : Rumah Sakit Charitas Palembang)

Abstract

Dengue Hemorrhagic Fever (DHF) is a type of disease that was ranked first in ASEAN and ranked second in the world. The number of victims of dengue in RS Charitas Palembang tend to increase in certain months and erratic every month. In addition, dengue casualty data is not used as an evaluation to reduce the number of victims. It became the basis for forecasting the number of victims of dengue in the next year. Research to predict the number of victims of dengue have been done with various techniques of artificial intelligence. Research conducted now use data RS Charitas Palembang patterned time series over the last 10 years by using Neural Network. The results obtained are patterns victim DBD significant start in December and then reach the peak in January, accompanied by figures forecast in each month of the following year. Furthermore, the calculation error using Neural Network obtained using the input layer 12, hidden neuron 28, and the output layer 1 and the error obtained 12.59%.