Performance Comparison Between Support Vector Regression and Artificial Neural Network for Prediction of Oil Palm Production
Abstract: The largest region
that produces oil palm in Indonesia has an important role in improving the
welfare of society and economy. Oil palm has increased significantly in Riau
Province in every period, to determine the production development for the next
few years with the functions and benefits of oil palm carried prediction
production results that were seen from time series data last 8 years
(2005-2013). In its prediction implementation, it was done by comparing the
performance of Support Vector Regression (SVR) method and Artificial Neural
Network (ANN). From the experiment, SVR produced the best model compared with
ANN. It is indicated by the correlation coefficient of 95% and 6% for MSE in
the kernel Radial Basis Function (RBF), whereas ANN produced only 74% for R2
and 9% for MSE on the 8th experiment with hiden neuron 20 and learning rate
0,1. SVR model generates predictions for next 3 years which increased between
3% - 6% from actual data and RBF model predictions.
Keywords: Artificial Neural
Network (ANN); Palm Oil; Prediction; Radial Basis Function (RBF), Support
Vector Regression (SVR)
Author: Mustakim, Agus Buono,
Irman Hermadi
Journal Code: jptkomputergg160002
