Peramalan Jumlah Kasus Penyakit Menggunakan Jaringan Saraf Tiruan Backpropagation (Studi Kasus Puskesmas Rogotrunan Lumajang) - Andika Harlan; Budi Darma Setiawan; Marji Marji (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Changes in the number of cases of disease is very influential on health improvement efforts both in terms of medicines availability, targeted medicines, damaged medicines and so forth. Knowing the pattern of the number of cases of disease is very important for some activities and jobs that exist. Therefore it is necessary to forecast the number of cases of disease to determine the pattern of the number of cases of disease in the future. One of the most common method of artificial neural network forecasting is Backpropagation. This study aims to forecast the number of cases of disease by using the case study of puskesmas Rogotrunan, Lumajang using Backpropagation method. Backpropagation parameters tested are the amount of data (n), alpha (Î±), and the number of iterations (epoch). Forecasting the number of disease on cases with test data from January to December of 2016 conducted using Backpropagation resulted in the value of MSE 115 and the accuracy of 0.0088.