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Peramalan KLBCampakMenggunakanGabunganMetode JST Backpropagationdan CART
https://doi.org/10.22146/ijccs.3495 Sulistyowati Sulistyowati(1*), Edi Winarko(2)
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(2)
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Abstract
Backpropagation neural network is one of the most commonly used methods for forecasting which can result in a better level of accuracy than other ANN methods. While the methods of CART is a binary tree method is also popular for the classification, which can produce models or classification rules.
Results of this study show that the number of the best window for backpropagation neural network to forecast the outcome affect forecasting accuracy. Determination of the number of windows of a backpropagation neural network forecasting on each attribute gives different results and directly affects the forecasting results. ANN can do the forecasting in time series using siliding window with accuracy 90.01% and then CART method can be use for classification with accuracy 83.33%.
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[1] Siswanti, K.Y., 2011, Model Fungsi Transfer Multivariat Dan Aplikasinya Untuk Meramalkan Curah Hujan Di Kota Yogyakarta, Skripsi, program studi Matematika, Fakultas Matematika dan Ilmu pengetahuan alam, Universitas Negeri Yogyakarta.
[2] Zhang, G.P., 2004, Neural Networks in Business Forecasting, Idea Group Publishing, Georgia State University, USA.
[3] Breiman, L., Friedman, J.H., Olsen, R.H. dan Stone, C.H., 1984, Classification and Regression Tress, Chapman and Hall, New York.
[4] Larose, T., 2005, Discovering Knowledge in Data an Introduction to Data Mining, Wiley Interscience, Canada.
[6] Santoso, B., 2007, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis, Graha Ilmu, Yogyakarta.
[7] Tsai, C.-F. dan Wang, S.-P., 2009, Stock Price Forecasting by Hibrid Machine Learning Technique, In In Proceedings of the 7th Asia Pacific Industrial Engineering and Management System Conference, Hongkong.
[8] Depkes RI Direktorat Jendral PP dan PL, 2008, Petunjuk Surveilans Campak, Jakarta
[9] Burse, K., Manoria , M. dan Kirar, V.P.S., 2010, Backpropagation Algorithm to Avoid Local Minima in Multiplicative Neuron Model, Word Academy of Science, Engineering and Technology
[10] Kara, Y., Boyacioglu, M.A. dan Baykan, O.K., 2010, Predicting Direction Of Stock Price Index Movement Using Artificial Neural Network And Support Vector Machines: The Sample Of The Istanbul Stock Exchange, Expert System with Applications, 38(Elvisier), pp.5311-19.
[11] Singh, S., Bhambri, P. dan Gill, J., 2011, Time Series Based Temperature Prediction Using Backpropagation with Genetic Algoritm Technique, International Journal of Computer Science, Vol.8(5, No. 3 September ).
[12] Devi, C.J., Reddy, B.S.P., dan Kumar, K.V., 2012, ANN Aproach for Weather Prediction using Back Propagation, Intenational Journal of Engineering Trends and Technology, Vol. 3, Pradesh
[13] Pratama, T.I.B., 1999, Metode Peramalan Memakai Jaringan Syaraf Buatan Dengan Cara Backpropagation, Jurnal Teknologi Industri, Vol.III, No.2, hal. 109-116.
[14] Han, J. dan Kamber, M., 2001, Data Mining: Concepts And Techniques, Morgan Kaufman, San Francisco.
DOI: https://doi.org/10.22146/ijccs.3495
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