Backpropagation Neural Networks Implementation for JKSE Forecasting
Seng Hansun
Seng Hansun, Informatics Department, Universitas Multimedia Nusantara, Tangerang, Indonesia.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9902-9905 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9281118419/2019©BEIESP | DOI: 10.35940/ijrte.D9281.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Time Series Analysis, Backpropagation, Neural Networks, JKSE Forecasting.
Scope of the Article: Sensor Networks, Actuators for Internet of Things.