GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA PERAMALAN DATA TIME SERIES
General Regression Neural Network (GRNN) is one method that was developed from the concept of artificial neural network that can be used for forecasting. This method was applied to predict the time series data that has a causal relations where the forecasting method used previously (ARIMA BOXJenkins...
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[Yogyakarta] : Universitas Gadjah Mada
2012
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id-ugm-repo.990852016-03-04T08:47:31Z https://repository.ugm.ac.id/99085/ GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA PERAMALAN DATA TIME SERIES , Luh Putu Widya Adnyani , Prof. Drs. H. Subanar, Ph.D ETD General Regression Neural Network (GRNN) is one method that was developed from the concept of artificial neural network that can be used for forecasting. This method was applied to predict the time series data that has a causal relations where the forecasting method used previously (ARIMA BOXJenkins) is not able to explain the presence of linkage data. This research was conducting by taking the dollar exchage rate and composite stock price index(IHSG). By using the GRNN methode will obtained the predictive value in some future periode. The advantages using this method is faster in term of computation and doesn�t requared the presence of a data asumptions. GRNN method produces more accurate predictive value comapred with ARIMA. It was shown that the MSE value is smaller than ARIMA [Yogyakarta] : Universitas Gadjah Mada 2012 Thesis NonPeerReviewed , Luh Putu Widya Adnyani and , Prof. Drs. H. Subanar, Ph.D (2012) GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA PERAMALAN DATA TIME SERIES. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=55212 |
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ETD , Luh Putu Widya Adnyani , Prof. Drs. H. Subanar, Ph.D GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA PERAMALAN DATA TIME SERIES |
| description |
General Regression Neural Network (GRNN) is one method that was
developed from the concept of artificial neural network that can be used for
forecasting. This method was applied to predict the time series data that has a
causal relations where the forecasting method used previously (ARIMA BOXJenkins)
is not able to explain the presence of linkage data.
This research was conducting by taking the dollar exchage rate and
composite stock price index(IHSG). By using the GRNN methode will obtained
the predictive value in some future periode. The advantages using this method is
faster in term of computation and doesn�t requared the presence of a data
asumptions. GRNN method produces more accurate predictive value comapred
with ARIMA. It was shown that the MSE value is smaller than ARIMA |
| format |
Theses and Dissertations NonPeerReviewed |
| author |
, Luh Putu Widya Adnyani , Prof. Drs. H. Subanar, Ph.D |
| author_facet |
, Luh Putu Widya Adnyani , Prof. Drs. H. Subanar, Ph.D |
| author_sort |
, Luh Putu Widya Adnyani |
| title |
GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA
PERAMALAN DATA TIME SERIES |
| title_short |
GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA
PERAMALAN DATA TIME SERIES |
| title_full |
GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA
PERAMALAN DATA TIME SERIES |
| title_fullStr |
GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA
PERAMALAN DATA TIME SERIES |
| title_full_unstemmed |
GENERAL REGRESSION NEURAL NETWORK (GRNN) PADA
PERAMALAN DATA TIME SERIES |
| title_sort |
general regression neural network (grnn) pada
peramalan data time series |
| publisher |
[Yogyakarta] : Universitas Gadjah Mada |
| publishDate |
2012 |
| url |
https://repository.ugm.ac.id/99085/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=55212 |
| _version_ |
1681230479105196032 |