Open AccessBackpropagation Neural Networks Implementation for JKSE Forecasting
Author(s) -
Seng Hansun
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d9281.118419
Subject(s) - backpropagation , artificial neural network , computer science , robustness (evolution) , time series , data mining , artificial intelligence , machine learning , stock market , stock exchange , composite index , econometrics , composite indicator , mathematics , paleontology , biochemistry , chemistry , finance , horse , biology , economics , gene
Neural networks is a type of soft computing methods that widely has been used and implemented in many fields, including time series analysis. One of the goals of time series analysis is to predict future data value.In this study, we try to implement another approach using the backpropagation neural networks method to forecast the Jakarta Stock Exchange (JKSE) composite index data, which is one of the stock market change indicators in Indonesia.The study then is continued by calculating the accuracy and robustness levels of Backpropagation NN in forecasting JKSE data. The experimental result on the case taken shows an encouraging and promising result.