PENERAPAN TRANSFORMASI DATA DISCRETE WAVELET TRANSFORM PADA NEURAL NETWORK UNTUK PREDIKSI HARGA SAHAM
Research on stock prices is still interesting for researchers. As in this study, ANTM's stock price closing data is used as a data set that is processed to be then predicted in the future. The Neural Network method is a method that is very widely used by researchers because of its various advantages. While the Discrete Wavelet Transform method is used to transform data to improve data quality so that it is expected to improve Neural Network performance. Based on experiments conducted by the Neural Network method with the Binary Sigmoid activation function which also carried out data transformation with Discrete Wavelet Transform, it has produced a smaller RMSE than prediction experiments without using data transformation with Discrete Wavelet Transform.
Keywords: Prediction, Stock Prices, Neural Network, Discrete Wavelet Transform
A, Adebiyi, A., K, Charles, A., O, Marion, A., & O, Sunday, O. (2012). Stock Price Prediction using Neural Network with Hybridized Market Indicators. Journal of Emerging Trends in Computing and Information Sciences, 3(1), 1–9.
Anbazhagan, S., & Kumarappan, N. (2014). Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT. Energy Conversion and Management, 78, 711–719. https://doi.org/10.1016/j.enconman.2013.11.031
Beaumont, A. N. (2014). Data transforms with exponential smoothing methods of forecasting. International Journal of Forecasting, 30(4), 918–927. https://doi.org/10.1016/j.ijforecast.2014.03.013
Bennett, C. J., Stewart, R. a., & Lu, J. W. (2014). Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system. Energy, 67, 200–212. https://doi.org/10.1016/j.energy.2014.01.032
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques.
Hofmann, M. (2009). Data Mining and Knowledge Discovery Series.
Kirchgässner, G., & Wolters, J. (2007). Introduction to Modern Time Series Analysis. https://doi.org/10.1007/978-3-540-73291-4
Montgomery, D. C. (2008). Introduction to Time Series Analysis and Forecasting.
Ouyang, Y., & Yin, H. (2014). A neural gas mixture autoregressive network for modelling and forecasting FX time series. Neurocomputing, 135, 171–179. https://doi.org/10.1016/j.neucom.2013.12.037
Rajput, V., & Bobde, S. (2016). Stock Market Forecasting Techniques: Literature Survey. International Journal of Computer Science and Mobile Computing, 5(6), 500–506. Retrieved from www.ijcsmc.com
Sundararajan, D. (2015). Discrete Wavelet Transform: A Signal Processing Approach. In Discrete Wavelet Transform: A Signal Processing Approach. https://doi.org/10.1002/9781119113119
Suryani, I. (2015). Penerapan Exponential Smoothing untuk Transformasi Data dalam Meningkatkan Akurasi Neural Network pada Prediksi Harga Emas. 1(2).
Abstract viewed = 126 times
PDF downloaded = 37 times
Copyright (c) 2019 Indah Suryani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Jurnal Riset Informatika as publisher of the journal, and the author also holds the copyright without restriction.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , are allowed with a written permission from Jurnal Riset Informatika.
Jurnal Riset Informatika, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in the Jurnal Riset Informatika are sole and exclusive responsibility of their respective authors and advertisers.