Beranda > Algoritma dan Metode, Ilmu Komputer, Informatika, Teknologi > Performance Comparison Between Support Vector Regression and Artificial Neural Network for Prediction of Oil Palm Production

Performance Comparison Between Support Vector Regression and Artificial Neural Network for Prediction of Oil Palm Production

Time Series Plot of Aktual, Model RBF, 2014, 2015, 2016Abstract: 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.

Conclusion:

Aktual, ANN-8From the conducted research, can be concluded that model SVR is better than model ANN for oil palm production prediction’s case in Riau. Model ANN got best value of determination coefficient (R2) 74% with galat error 9% on the 8th experiment, while SVR on the RBF kernel produced a smaller galat is 6% and also R2 is bigger than ANN that produced 95%. A very huge difference of determination coefficient value proved that by using time series data, model SVR is more superior compared to model ANN. Prediction results for next three years rise step by step in normal form as many as 3%-6%. Prediction results do not reckon the nature or other factors in the field that could effect production in each period.

Mustakim – Computer Science and Information Journal – Indonesia University

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