Optimasi Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Penduduk Nasional

Abstract

Predictions or forecasting are important parameters in determining policy that will be implemented in the future. The higher the population of a country, the more it will affect the balance of that country. This research aims to predict the national population using the Artificial Neural Network (ANN) approach. The sample for this research is population data for each province obtained from the National Central Statistics Agency (BPS). The data is iterated from 2011 to 2022. The approach method uses backpropagation Artificial Neural Networks (ANN). The architectural model is determined with 2 hidden layers and 1 output, namely 5-20-10-1 with learning rate variations of 0.01, 0.02, and 0.03. ANN prediction results are able to predict the national population. The success of the three models shows that the ANN model with a Learning rate of 0.01 has the highest accuracy level of 96.9697, equivalent to 97%. This is proven by the model's success in predicting 32 out of 33 data samples with a time duration of 117 seconds. The prediction results showed that the highest population numbers were in three provinces, namely Central Java, West Java, and East Java. Meanwhile, the lowest population is in West Papua province. Keywords: BPS, JST, Learning Rate, Optimization.