Forecasting of stock prices on the Indonesian Sharia Stock Index (ISSI) using backpropogation artificial neural network

Abstract

Forecasting is a method for estimating a value in the future by looking at data in the past. In this study, the author will discuss the results of forecasting the price of the Islamic stock index using a backpropogation artificial neural network. The results show that the forecasting value of the stock closing price at ISSI for the next 1 period April 2018 is an average of 192,6842. After getting the forecast value, then it is compared with the actual average data, which is 185.4748. the amount of error generated greatly affects the number of inputs and the selection of the right network architecture pattern.   Keywords: Prediction, Backpropogation, ISSI