APLIKASI JARINGAN SYARAF TIRUAN BERBASIS RADIAL UNTUK MENENTUKAN PREDIKSI WAKTU PENGERINGAN GABAH PADA PENGERING RADIASI INFRA MERAH
Abstract: The aim of this
research is for predicting the timeof drying on infrared radiation dryer by
using artificial neural
network with Radial
Base Function (RBF) Algorithm. The RBF neural network was
created and trained by using set of data that
are taken from
actual measurement. The
parameters are used
on network training namely
radiation intensity on preheating and main heating stage, variety input power
of the infra
red on preheating
and main heating stage,
kinds of reflector on
main heating stage
and thickness of
paddy for determining
drying time. There were 36 variations data, includes 26 data are taken
as training data for the net to learn. The rest data will be taken as test data for the net. Results showed that
prediction data were closed to actual results. The MSE for the drying time prediction for training
and testing average byusing RBF showed 0.0003 % and 2.62%
respectively compare to
backpropagation 1.064% and
6.595% respectively. For the
computation time showed
RBF was faster
than backpropagation. The time
computation results for
RBF and backpropagation achieved 0.511
sec and 28
sec respectively. From
above result, it
can be concluded that RBF is very
fast and accurate for predicting drying time on infra red radiation dryer model
compare to backpropagation.
Keywords: Artificial neural
network, RBF, Backpropagation, Prediction, Drying time
Kode Jurnal: jptmesindd100128
