Interpolasi Kriging Dalam Pemodelan Gstar-Sur Dan Gstarx-Sur Pada Serangan Hama Penggerek Buah Kopi

Abstract

The GSTAR and GSTARX models normally can only be formed from observed locations. The problem that sometimes occurs is that not all locations that want to be modeled have complete data as well as other locations. This study uses GSTAR and GSTARX modeling using SUR approach and combines them with the kriging interpolation technique for forecasting coffee berry borer attack in Probolinggo Regency. This modeling is called GSTAR-SUR Kriging and GSTARX-SUR Kriging. This study aims to determine the best model between GSTAR-SUR Kriging and GSTARX-SUR Kriging for forecasting coffee borer attack in an unobserved location. The result of this study shows that GSTAR-SUR Kriging and GSTARX-SUR Kriging models can be used for forecasting coffee berry borer attack in unobserved locations with high forecast accuracy shown by MAPE values <10%. In this study the GSTARX-SUR Kriging model (1,[1,12])(10,0,0) is the best model for forecasting boffee berry borer attacks in unobserved locations.

The Application of the Semiparametric Gstar Model in Determining Gamma-Ray Log Data on Soil Layers

“…For example, the GSTAR(1;1) model was carried out with modification of spatial weights through railroad passenger's mobility to research Covid-19 growth data in Java Island (Pasaribu et al, 2021). Further, the GSTAR-Kriging model was used in rainfall data (Abdullah et al, 2018) while the GSTAR-SUR Kriging model was applied to the data of coffee borer beetle attacks in East Java (Pramoedyo et al, 2020). The developed model is parametric because the model parameters must be estimated.…”