UNP Journal of Statistics and Data Science
Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science

Backpropagation Neural Network Application in Predicting The Stock Price of PT Bank Rakyat Indonesia Tbk

Dewi Febiyanti (Universitas Negeri Padang)
Nonong Amalita (Unknown)
Dony Permana (Unknown)
Tessy Octavia Mukhti (Unknown)



Article Info

Publish Date
30 Nov 2023

Abstract

Investors often make mistakes when making stock transactions even though having chosen good company stocks. The thing that needs to be considered in making stock transactions is to see the movement of stock prices. The movement of the stock price in PT Bank Rakyat Indonesia Tbk has changed in the form of a decrease or increase. The increase in stock price will provide benefits for investors by selling stocks. However, the occurrence of mistakes when choosing the time to make stock transactions results in investors being able to take high risks because stock prices fluctuate. Therefore, to anticipate the occurrence of high risk to investors, stock price predictions is made using a Backpropagation Neural Network (BPNN). BPNN can adapt quickly and is able to predict nonlinear data such as stock prices and produce a high level of accuracy. The results of this study obtained the best BPNN model, namely the BP(5,3,1) model with a Mean Absolute Percentage Error (MAPE) of 0,8193%. These results show that the model has good network performance so that it can predict stock prices well because it gets a small prediction error

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Journal Info

Abbrev

ujsds

Publisher

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

Description

UNP Journal of Statistics and Data Science is an open access journal (e-journal) launched in 2022 by Department of Statistics, Faculty of Science and Mathematics, Universitas Negeri Padang. UJSDS publishes scientific articles on various aspects related to Statistics, Data Science, and its ...