| dc.contributor.author | Buono, Agus | |
| dc.contributor.author | Agmalaro, Muhammad Asyhar | |
| dc.contributor.author | Almira, Amalia Fitranty | |
| dc.date.accessioned | 2016-05-21T03:01:22Z | |
| dc.date.available | 2016-05-21T03:01:22Z | |
| dc.date.issued | 2014-10 | |
| dc.identifier.isbn | 978-979-1421-22-5 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/80641 | |
| dc.description.abstract | Abstract-Indonesia has abundant natural
resources in agriculture. Good agricultural resuIts
can be obtained by deter mining a good growing
season plan. One of important factors which
determines the successful of crop is the length of
the rainy season. The length of the rainy season is
dynamic and difficuIt to be controlled. Therefore
the planning of the growing season becomes
inaccurate and cause crop failures. This research
aims to develop a model to predict the length of the
rainy season using time delay neural network
(TDNN). Observattonal data used in this research
is the length of rainy season from three weather
and climate stations of the Pacitan region from
1982/1983 to 2011/2012. Predictor data used in this
reserach is sea surface temperature (SST) derived
from the region of Nino 1+2, Nino 3, Nino 4, and
Nino 3.4 from 1982 to 2011. Model with the best
accuracy was obtained by Pringkuku station with
RMSE of 1.97 with pararneters of delay 10 1 2 3\,
learning rate 0.1, 40 hidden neurons, and
predictors of Nino 3 and R-squared of 0.82 with
pararneters of delay 10 1\, learning rate 0.3, 5
hidden neurons, and predictors of Nino 3. | id |
| dc.language.iso | en | id |
| dc.publisher | Bogor Agricultural University (IPB) | id |
| dc.publisher | Bogor Agricultural University (IPB) | id |
| dc.title | Forecasting the Length of the Rainy Season Using Time Delay Neural Network | id |
| dc.type | Article | id |
| dc.subject.keyword | time delay neural network (TDNN). | id |
| dc.subject.keyword | sea surface temperature (SST | id |
| dc.subject.keyword | the length of rainy season | id |
