Analysis of Backpropagation Method with Sigmoid Bipolar and Linear Function in Prediction of Population Growth

Abstract

Backpropagation method is an artificial neural network method that is often used for prediction. However, the use of activation functions and training functions greatly affects the accuracy of a prediction. In this study will discuss the backpropagation method by applying the activation function of Sigmoid bipolar and linear to predict population growth in Simalungun regency, Indonesia. The purpose of this paper is to look at the level of population growth in the district so that the government has a benchmark in determining policies so that a surge in population growth can be minimized and that the government pays more attention to the level of welfare of its population. As for academics, this research can be used as input if you want to do a prediction or forecasting with different cases. The data used in this paper is population density data in Indonesia's Simalungun district, which is sourced from the Simalungun regency statistics center of Indonesia. This study uses 5 architectural models, namely 3-5-1, 3-10-1, 3-5-10-1, 3-5-15-1 and 3-10-15-1. Of these 5 models, the best architectural model is 3-5-10-1 with an accuracy of 97% and an MSE value of 0.00034833. Minimum Error 0,001-0,01 and learning rate 0,01.