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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of ICT Research and Applications Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Ilmu Komputer dan Bisnis PREPOTIF : Jurnal Kesehatan Masyarakat ProBisnis : Jurnal Manajemen
Ignatius Wiseto Prasetyo Agung
Universitas Adhirajasa Reswara Sanjaya (ARS University)
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Input Parameters Comparison on NARX Neural Network to Increase the Accuracy of Stock Prediction Ignatius Wiseto Prasetyo Agung
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7158

Abstract

The trading of stocks is one of the activities carried out all over the world. To make the most profit, analysis is required, so the trader could determine whether to buy or sell stocks at the right moment and at the right price. Traditionally, technical analysis which is mathematically processed based on historical price data can be used. Parallel to technological development, the analysis of stock price and its forecasting can also be accomplished by using computer algorithms e.g. machine learning. In this study, Nonlinear Auto Regressive network with eXogenous inputs (NARX) neural network simulations were performed to predict the stock index prices. Experiments were implemented using various configurations of input parameters consisting of Open, High, Low, Closed prices in conjunction with several technical indicators for maximum accuracy. The simulations were carried out by using stock index data sets namely JKSE (Indonesia Jakarta index) and N225 (Japan Nikkei index). This work showed that the best input configurations can predict the future 13 days Close prices with 0.016 and 0.064 mean absolute error (MAE) for JKSE and N225 respectively. 
Co-Authors Acep Rohendi Agustin, Dari Dianata Alfaruk, Muhammad Harist Ardi, Wildan Khalifah Bambang Riyanto Erfian Junianto Erliany Syaodih Hidayat, Fajar Mukti Junaedi, Novan Mayangsari, Ariefa Diah Muhammad Rizqi Prawasari, Nindyan Rabbani, Muhammad Ihsan Ramadhan, Fanky Fazdianki Rinaldi Munir Rizal Rachman, Rizal Salam, Regi Sarwono Sutikno Sarwono Sutikno SATRIYAS ILYAS Toni Arifin Wibowo, Ilham Rachmat