PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) DENGAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK MENGGUNAKAN GUI MATLAB

*Rizki Brendita Br Tarigan  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Hasbi Yasin  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Alan Prahutama  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Published: 30 Nov 2018.
Open Access Copyright 2020 Jurnal Gaussian
License URL: http://creativecommons.org/licenses/by-nc-sa/4.0

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Abstract
Capital market Indonesia is one of the important factors in the development of the national economy, proved to have many industries and companies that use these institutions as a medium to absorb investment to strengthen its financial position. The recent years, Jakarta Composite Index (JCI) in Capital Market tend to strengthen. JCI data are the time series data obtained from the past to predict the future with caracteristics of JCI data are non stationary and non linier. Neural network is a computational method that imitate the biological neural network. There are several types of methods that can be used in neural network that is: Radial Basis Function Neural Network (RBFNN) Generalized Regression Neural Network (GRNN), dan Probabilistic Neural Network (PNN). Model of Radial Basis Function Neural Network is suitable for time series data. This model has a network architecture in the form of input layer, hidden layer and output layer. This research is done with the help of GUI as a computation tool. The results of analysis by using GUI conducted on the size sample of data as much as 1211 taken as 100 the data thus obtained value of 2315,6 MSE training and training MAPE value of 0,72%, while for the testing of 28886,7 MSE and MAPE testing value is 0,70%. Based on the results of forecasting, JCI values on January 02, 2018 until January 08, 2018 at 6499,922 every day.

 

Keywords: Radial Basis Function Neural Network (RBFNN), Jakarta Composite Index (JCI), MSE, MAPE, Time Series, GUI.

Keywords: Radial Basis Function Neural Network (RBFNN), Jakarta Composite Index (JCI), MSE, MAPE, Time Series, GUI.

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