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ITS » Master Theses » 51200-Teknik Informatika S2
Posted by tondoindra@gmail.com at 08/06/2015 17:45:08  •  1297 Views


PREDIKSI HARGA EMAS MENGGUNAKAN METODE GENERALIZED REGRESSION NEURAL NETWORK DAN ALGORITMA GENETIKA

GOLD PRICE PREDICTION USING GENERALIZED REGRESSION NEURAL NETWORK AND GENETIC ALGORITHM

Author :
MARTHASARI, GITA INDAH ( 5111202002 )




ABSTRAK

Prediksi harga emas merupakan aktivitas penting bagi banyak pihak. Salah satu metode prediksi harga emas yang dapat digunakan adalah jaringan syaraf tiruan berbasis generalized regression neural network GRNN. Dalam penelitian sebelumnya GRNN digabungkan dengan teknik dekomposisi Seasonal Trend Decomposition based on Locally Weighted Regression STL dan metode theta. Kinerja GRNN dipengaruhi oleh data latih yang digunakan karena ukuran jaringan yang terbentuk akan berbanding lurus dengan jumlah data latih. Untuk mengatasi meningkatnya ukuran jaringan seiring dengan bertambahnya data latih proses reduksi data latih tanpa mengurangi akurasi prediksi perlu dilakukan. Dalam penelitian ini metode peramalan GRNN diintegrasikan dengan algoritma genetika untuk mereduksi data latih guna menghasilkan model peramalan yang lebih efisien. Sebelum diramalkan data harga emas didekomposisi menggunakan STL menjadi komponen data musiman tren dan residual. Ketiga komponen data tersebut diramalkan menggunakan dua metode yang berbeda yaitu GRNN untuk meramalkan komponen data musiman dan residual dan metode theta untuk meramalkan komponen data tren. Hasil peramalan dari ketiga komponen tersebut selanjutnya digabungkan menggunakan jaringan syaraf tiruan propagasi balik untuk memperoleh hasil peramalan akhir. Hasil pengujian menunjukkan bahwa GRNN yang diintegrasikan dengan algoritma genetika selain mampu menghasilkan peramalan dengan akurasi yang setara dengan GRNN tanpa algoritma genetika juga mampu memberikan akurasi yang lebih baik dibandingkan dengan hasil permalan menggunakan model peramalan Arima. Selain itu kombinasi GRNN dengan algoritma genetika mampu mereduksi jumlah data latih sebesar 50 dan mampu mengurangi waktu proses peramalan sebesar 34.


ABSTRACT

The prediction of gold price is an important activity for all parties. One of the gold prediction methods that can be used is the artificial neural network based on the generalized regression neural network GRNN. In the previous research GRNN was combined with the decomposition technique of Seasonal Trend Decomposition based on locally weighted regression STL and the Theta method. The GRNN performance was influenced by thetraining data size usedsince the network size formed is proportionally dependent on the training data size. To cope with the increasing of the network size along with the increasing of the training data size the process of training data reduction that is capable of maintaining the accuracy of the prediction is interesting to investigate. In this research the GRNN prediction method is integrated with the genetic algorithm in order to reduce the training data sizethat will in turn produce the more efficient prediction model. Initially the gold price data is decomposed into three components i.e. seasonal trend and residual data using STL decomposition technique. Those three components are then predicted using two different methods namely GRNN to predict the component of the seasonal and residual data and the theta method to predict the trend data component. The prediction results of those three components are combined together using the back propagation neural network algorithm to obtain final results of the prediction. Experimental results showed that the GRNN method that was integrated with the genetic algorithm was not only capable of producing prediction results with the accuracy similar to those produced using the original GRNN method but also capable of giving the better prediction accuracy in compared to that produced using ARIMA prediction model. In addition to that the combination of GRNN with the genetic algorithm is also capable of reducing the amount of the training data as much as 50 and reducing the computing time consumed by the prediction process as much as 34.



Keywordsperamalan harga emas, optimasi data latih, algoritma genetika, dekomposisi data runut waktu, generalized regression neural network, metode theta
 
Subject:  Analis Regresi
Contributor
  1. Prof. Ir. Arif Djunaidy, M.Sc., Ph.D.
Date Create: 05/02/2014
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-Master-51103150001245
Collection ID: 51103150001245
Call Number: RTIf 005.1 Mar p


Source
Master Theses of informatics Engineering RTIf 005.1 Mar p, 2015

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Copyright @2015 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




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  1.  ITS-Master-37790-5111202002_Abstract_in.pdf - 385 KB
  2.  ITS-Master-37790-5111202002_Abstract_en.pdf - 434 KB
  3.  ITS-Master-37790-5111202002_Conclusion.pdf - 396 KB




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