Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis

Rezzy Eko Caraka (Bioinformatics and Data Science Research Center, Bina Nusantara University, Anggrek Campus Room 700, Jakarta, Indonesia)
Hasbi Yasin (Departemen Statistika Universitas Diponegoro, Semarang)
Adi Waridi Basyiruddin (Departemen Statistika Universitas Diponegoro, Semarang)

Jurnal Matematika
Vol 7 No 1 (2017)

Recently, instead of selecting a kernel has been proposed which uses SVR, where the weight of each kernel is optimized during training. Along this line of research, many pioneering kernel learning algorithms have been proposed. The use of kernels provides a powerful and principled approach to modeling nonlinear patterns through linear patterns in a feature space. Another bene?t is that the design of kernels and linear methods can be decoupled, which greatly facilitates the modularity of machine learning methods. We perform experiments on real data sets crude palm oil prices for application and better illustration using kernel radial basis. We see that evaluation gives a good to fit prediction and actual also good values showing the validity and accuracy of the realized model based on MAPE and R2. Keywords: Crude Palm Oil; Forecasting; SVR; Radial Basis; Kernel