| dc.description.abstract | Stock price movements are very volatile from time to time. The stock
price movement is influenced by many factors, including company performance,
dividend risk, the country’s economic conditions, and inflation rate. The existence
of these complex factors makes stock price movements challenging to predict.
Investors need stock price predictions to see the company’s stock investment
prospects in the next period. The method that can predict stock prices is
Backpropagation. The Backpropagation method is an algorithm that adopts a human
mindset systematically to minimize the error rate by adjusting the weights based on
differences in output and the desired target. This study uses historical stock index
data for LQ45 from February 26, 2019 – February 26, 2021, namely the closing price
as an input and the opening price as the target. The best network model from the
Backpropagation method uses a binary sigmoid activation function with nine
neurons in the hidden layer. The testing accuracy value is 95.2481% (MAPE), and
the error value is 0.000266 (MSE). The error value shows that the prediction model
results are excellent | en_US |
