Implementasi Jaringan Saraf Tiruan Backpropagation untuk Memprediksi Jumlah Penduduk Miskin di Indonesia dengan Optimasi Algoritme Genetika

Arthur Julio Risa Ashshiddiqi (Fakultas Ilmu Komputer, Universitas Brawijaya)
Indriati Indriati (Fakultas Ilmu Komputer, Universitas Brawijaya)
Sutrisno Sutrisno (Fakultas Ilmu Komputer, Universitas Brawijaya)

Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vol 2 No 11 (2018): November 2018

Poverty is a common issues encountered by every country, and Indonesia is one of them. The escalation of the poor occurred almost every year. According to Indonesia Statistic Bureau (Badan Pusat Statistik) using population indicator based on their monthly expense below the line of poverty can be categorized as poor people. The increasing amount of the poor can trigger criminality, that is because those individuals will do anything to make ends meet. By predicting the amount of the poor, hopefully the government or any related institution can help decrease poverty and unemployment rate in Indonesia. Artificial neural network backpropagation is one of the method that can be used to make predictions. Weight and bias in backpropagation's training optimized using genetic algorithm to obtain more optimal results. In this artificial neural network backpropagation research method that the weight training optimized using genetic algorithm generate 8.744579% AFER points.