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INTERPOLASI KRIGING DALAM PEMODELAN GSTAR-SUR DAN GSTARX-SUR PADA SERANGAN HAMA PENGGEREK BUAH KOPI

Henny Pramoedyo  -  Jurusan Statistika, Fakultas MIPA, Universitas Brawijaya, Indonesia
*Arif Ashari  -  Jurusan Statistika, Fakultas MIPA, Universitas Brawijaya, Indonesia
Alfi Fadliana  -  Jurusan Statistika, Fakultas MIPA, Universitas Brawijaya, Indonesia
Open Access Copyright (c) 2020 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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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.

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Keywords: GSTAR; GSTARX; Forecasting; Kriging Interpolation; Coffee Berry Borer

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  1. Abdullah, A. S., Matoha S., Lubis D. A., Falah A. N., Mindra Jaya A. G. N., Hermawan E. & Ruchjana B. N., Implementation of Generalized Space Time Autoregressive (GSTAR)-Kriging Model for Predicting Rainfall Data at Unobserved Locations in West Java, Applied Mathematics & Information Sciences, 2018, Vol. 12, No. 3: 607-615
  2. Alaba, O. O., Olubusoye E. O., & Ojo S. O., Efficiency of Seemingly Unrelated Regression Estimator over The Ordinary Least Square, European Journal of Scientific Research, 2010, Vol. 39, No. 1: 153-160
  3. Baker, P. S., Barerra J. F., and Rivas A., Life History Studies of the Coffee Berry Borer (Hypothenemus hampei, Scolytidae) on Coffee Trees in Southern Mexico, Applied Ecology, 1992, Vol. 29: 656-622
  4. Borovkova, S., Lopuhaa H. P., and Ruchjana B. N., Consistency and Asymtotic Normality of Least Squares Estimators in Generalized STAR Models, Statistica Neerlandica, 2008, Vol. 62: 482-508
  5. Cressie, N. A.C., Statistics for Spatial Data, John Wiley and Sons, Inc., New York, 1993
  6. Damon, A., A Review of the Biology and Control of the Coffee Berry Borer Hypothenemus hampei (Coleoptera: Scolytidae). Bulletin of Entomological Research, 2000, Vol. 90, 453–465
  7. Gaetan, C. & Guyon, X., Spatial Statistics and Modeling, Media, 2010, URL: http://doi.org/10.1007/978-0-387-92257-7
  8. Infante, F., Pérez, J., & Vega, F. E., Redirect Research to Control Coffee Pest, Nature, 2012, 489-502
  9. Iriany, A., Suhariningsih, Ruchjana B. N. & Setiawan, Prediction of Precipitation Data at Batu Town using GSTAR (1,p)-SUR Model, Journal of Basic and Applied Scientific Research, 2013, Vol. 3, No. 6: 860-865
  10. Jaramillo, J., Borgemeister C. and Baker P., Coffee Berry Borer Hypothenemus hampei (Coleoptera: Curculionidae): Searching for Sustainable Control Strategies, Bulletin of Entomological Research, 2006, Vol. 96: 223– 233
  11. Setiawan, Suhartono dan Prastuti M., S-GSTAR-SUR Model for Seasonal Spatio Temporal Data Forecasting, Malaysian Jounal of Mathematical Sciences, 2016, Vol.10:53-65
  12. Webster, R. dan Oliver, M. A., Sample Adequately to Estimate Variograms of Soil Properties, J Soil Sci, 1992, Vol. 43, No. 1: 177-192
  13. Widyaningsih, A., Susilawati M., & Sumarjaya I. W., Estimasi Model Seemingly Unrelated Regression (SUR) dengan Metode Generalized Least Square (GLS), Jurnal Matematika, 2014, Vol. 4, No. 2: 102-110

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