Prediksi Harga Emas Menggunakan Metode Neural Network Backropagation Algoritma Conjugate Gradient
DOI:
https://doi.org/10.31961/eltikom.v1i2.21Keywords:
Conjugate Gradient, Artificial Neural Network, Backpropagation, Prediksi, EmasAbstract
Artificial Neural Network Backpropagation is known as one of the most reliable methods of predicting. The algorithm used in this research is Conjugate Gradient algorithm, with gold data data of input data for training data. The price of gold becomes an issue in the market, as a precious metal that can be used for investment is very interesting to make a gold price prediction application. Gold prices continue to increase in the world market, making investors interested to invest in this precious metal. The application of gold price prediction will be very useful for investors of precious metals. Gold price data used in this research is daily data, taken 3 (three) last year and divided into test data and data testing. Test data is used to generate new weights for data testing. The parameters used in the measurement of evaluation of predicted results from Conjugate Gradient algorithm Artificial Neural Network Backpropagation method is Meant Square Error (MSE), where the result of MSE from this research is 0.0313651
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References
M. Kangaranifarahani and S. Mehralian, “Comparison Between Artificial Neural Network and Neuro-Fuzzy for Gold Price Prediction,†2013.
S. Faisal et al., “Gold Price Prediction Using Radial Basis Function Neural Network,†2011.
Y. Sari and R. A. Pramunendar, “Classification Quality of Tobacco Leaves as Cigarette Raw Material Based on Artificial Neural Networks,†Int. J. Comput. Trends Technol., vol. 50, no. 3, pp. 147–150, 2017.
A. Y. Al Bayati, N. A. Sulaiman, and G. W. Sadiq, “A Modified Conjugate Gradient Formula for Back Propagation Neural Network Algorithm,†vol. 5, no. 11, pp. 849–856, 2009.
M. Cilimkovic, “Neural Networks and Back Propagation Algorithm,†Fett.Tu-Sofia.Bg, 2010.
K. Husnul and S. Yuslena, “Prediksi kualitas hasil hutan lahan basah menggunakan Backpropagation,†JTIULM, vol. 1, pp. 1–9, 2016.
D. Gries and F. B. Schneider, Texts in Computer Science, vol. 42. Verlag London: Springer, 2010.
B. C. Chang and Y. Hung, “A Neural Network-Based Prediction Model in Embedded Processes of Gold Wire Bonding Structure for Stacked Die Package,†vol. 97, no. 1, 2009.
R. F. F. S. C. R Development Core Team, R. E. Millsap, and C. W. J. Chase, “R: A Language and Environment for Statistical Computing,†J. Bus. Forecast. Methods Syst., vol. 14, no. 3, pp. 461–473, 2014.
T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,†Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, 2014.
S. Jachner, K. G. van den Boogaart, and T. Petzoldt, “Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV),†J. Stat. Softw., vol. 22, no. 8, pp. 1–30, 2007.
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