Forecasting and Mapping Coffee Borer Beetle Attacks Using GSTAR-SUR Kriging and GSTARX-SUR Kriging Models
DOI:
https://doi.org/10.21512/comtech.v11i2.6389Keywords:
coffee borer beetle, Generalized Space Time Autoregressive (GSTAR), GSTARX, Seemingly Unrelated Regression (SUR) , KrigingAbstract
The research aimed to use Generalized Space Time Autoregressive (GSTAR) and GSTARX modeling with the Seemingly Unrelated Regression (SUR) approach and combine them with the Kriging interpolation technique in an unobserved location. The case study was coffee borer beetle forecasting in Probolinggo Regency, East Java, Indonesia, with Watupanjang Village as the unobserved location. The results show that GSTAR-SUR Kriging and GSTARX-SUR Kriging models can predict coffee borer beetle attacks in unobserved areas with high accuracy. It is indicated by the Mean Absolute Percentage Error (MAPE) values of less than 10%. The addition of exogenous variables (rainfall) into the model is proven to improve the accuracy of the model. The Root-Mean-Square Error (RMSE) value of the GSTARX-SUR Kriging model is smaller than the GSTAR-SUR Kriging model. The structure of the model produced from the research, GSTARX-SUR (1,[1,12])(10,0,0), can be used as a reference in modeling coffee borer beetle attacks in other regencies. Map of forecasting coffee borer beetle attack shows that the spread of coffee borer beetle attack is spatial clustering with the attack center located in the eastern region of Probolinggo Regency.
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References
Abdullah, A. S, Matoha, S., Lubis, D. A., Falah, A. N., Jaya, I. G. N. M., Hermawan, E., & Ruchjana, B. N. (2018). Implementation of Geralized Space Time Autoregressive (GSTAR)-Kriging model for predicting rainfall data at unobserved locations in West Java. Applied Mathematics & Information Sciences, 12(3), 607-615. https://doi.org/10.18576/amis/120316
Andayani, N., Sumertajaya, I. M., Ruchjana, B. N., & Aidi, M. N. (2017). Development of space time model with exogenous variable by using transfer function model approach on the rice price data. Applied Mathematical Sciences, 11(36), 1779-1792. https://doi.org/10.12988/ams.2017.74150
Astuti, D., Ruchjana, B. N., & Soemartini. (2017). Generalized Space Time Autoregressive with exogenous variable model and its application. Journal of Physics: Conference Series, 893, 1-9. https://doi.org/10.1088/1742-6596/893/1/012038
Baker, P. S., Barrera, J. F., & Rivas, A. (1992). Life-history studies of the coffee berry borer (Hypothenemus Hampei, Scolytidae) on coffee trees in Southern Mexico. Journal of Applied Ecology, 29(3), 656-662. https://doi.org/10.2307/2404473
Borovkova, S., Lopuhaä, H. P., & Ruchjana, B. N. (2008). Consistency and asymptotic normality of least squares estimators in generalized STAR models. Statistica Neerlandica, 62(4), 482-508. https://doi.org/10.1111/j.1467-9574.2008.00391.x
Cressie, N. A. C. (1993). Statistics for spatial data (Revised edition). Hoboken, NJ: John Wiley & Sons.
Damon, A. (2000). A review of the biology and control of the coffee berry borer, Hypothenemus Hampei (Coleoptera: Scolytidae). Bulletin of Entomological Research, 90(6), 453-465. https://doi.org/10.1017/S0007485300000584
Gaetan, C., & Guyon, X. (2010). Statistics for spatial models. In C. Gaetan & X. Guyon (Ed.), Spatial statistics and modeling (pp. 149-248). Springer.
Greene, W. H. (2012). Econometric analysis (7th ed). Pearson.
Infante, F., Pérez, J., & Vega, F. E. (2012). Redirect research to control coffee pest. Nature, 489(7417), 502-502. https://doi.org/10.1038/489502a
Iriany, A., Suhariningsih, Ruchjana, B. N., & Setiawan. (2013). Prediction of precipitation data at Batu Town using the GSTAR(1,p)-SUR model. Journal of Basic and Applied Scientific Research, 3(6), 860-865.
Jaramillo, J., Borgemeister, C., & Baker, P. (2006). Coffee berry borer Hypothenemus Hampei (Coleoptera: Curculionidae): Searching for sustainable control strategies. Bulletin of Entomological Research, 96(3), 223-233. https://doi.org/10.1079/BER2006434
Nisak, S. C. (2016). Seemingly unrelated regression approach for GSTARIMA model to forecast rain fall data in Malang southern region districts. CAUCHY: Jurnal Matematika Murni dan Aplikasi, 4(2), 57-64. https://doi.org/10.18860/ca.v4i2.3488
Ruchjana, B. N., Borovkova, S. A., & Lopuhaa, H. P. (2012). Least squares estimation of Generalized Space Time AutoRegressive (GSTAR) model and its properties. AIP Conference Proceedings, 1450, 61-64. https://doi.org/10.1063/1.4724118
Setiawan, Suhartono, & Prastuti, M. (2016). S-GSTAR-SUR model for seasonal spatio temporal data forecasting. Malaysian Journal of Mathematical Sciences, 10(S), 53-65.
Suhartono, Wahyuningrum, S. R., Setiawan, & Akbar, M. S. (2016). GSTARX-GLS model for spatio-temporal data forecasting. Malaysian Journal of Mathematical Sciences, 10(S), 91-103.
Webster, R., & Oliver, M. A. (1992). Sample adequately to estimate variograms of soil properties. Journal of Soil Science, 43(1), 177-192. https://doi.org/10.1111/j.1365-2389.1992.tb00128.x
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