Rainfall Forecasting Using Gstar-Sur-Nn Approach in West Java Province

  • Authors

    • Atiek Iriany
    • Wayan Firdaus Mahmudi
    • Waego Hadi Nugroho
    • Agus Dwi Sulistyono
    2019-01-26
    https://doi.org/10.14419/ijet.v8i1.9.26392
  • potato, GSTAR-SUR

  • Potato is one of food commodities which is expected to serve as a diversification option of carbohydrate source. One of the negative factors to influence the productivity of potato in the last few years is uncertain climate condition. This problem can be overcome with the development of season forecasting method that produces reliable season forecasting model and has a precise forecasting accuracy, especially in extreme climate conditions. The second year stage of study has reached 70% in completion which includes the implementation of Year I research result, surveying potential locations for potato plants by digging information from farmers and agriculture experts in West Java, the identification of potato crop, identification of factors that influence potato crop growth, identification of rainfall patterns in West Java and exploration of rainfall data of each research rain post. From data exploration and data identification, GSTAR model((1,2,3,4,13,33)(1)–Sur is obtained.

     

     

  • References

    1. [1] Borovkova, S.A., Lopuha, H.P. and Ruchjana, B.N. 2002. Generalized S-TAR with Random Weights. Proceeding of the 17th International Workshop on Statistical Modeling. Chania-Greece.

      [2] Davidson, Russell and MacKinnon, James G. 1993. Estimation and Inference in Economics. Oxford University Press. UK.

      [3] Greene, W.H. 2002. Econometric Analysis (5thed). Prentice Hall.

      [4] Iriany, A, (2013), Climate Change Impact to Potato Farming in The Java of Indonesia in The Mountain Range of Batu East Java Indonesia, Journal of Applied Environmental and Biological Science, 3(6): 48-55.

      [5] Iriany, A., Suhariningsih, Ruchjana, B.N., and Setiawan. 2013. Prediction of Precipitation Data at Batu Town using The GSTAR(1,p)-SUR. Journal of Basic and Applied Scientific Research.3(6): vol 3 no 6.

      [6] Nainggolan, Nelson. 2012. Development of GSTAR Model with Assumption ARCH and its Application to Inflation. Dissertation Unpublihed. Bandung: ITB.

      [7] Pfeifer, P.E and Deutsch, S.J. 1980a. Stationarity and Invertibility Regions for Low Order STARMA Models. Communications in Statistics-Simulation and Computation 9 (5). P. 551-562.

      [8] 1980b. Identification and Interpretation of First Orde Space-Time ARMA Models.Technometrics, 22 (1), 397-408.

      [9] Ruchjana, B.N. 2001. Study on the Weight Matrix in the Space-Time Autoregressive Model. Proceeding of the Tenth International Symposium on Applied Stochastic Models and Data Analysis (ASMDA). Gerard Govaert, et al. Editor. Universite de Technologie Compiegne. Prancis. Vol. 2/2. p. 789-794.

      [10] .2002.A Generalized Space Time Autoregressive Model and its Application to Oil Production Data. Dissertation Unpublihed. Bandung : ITB.

      [11] Suhartono dan Atok, R.M. 2006. Pemilihan Bobot Lokasi yang Optimal pada Model GSTAR. Prosiding Konferensi Nasional Matematika XIII, Universitas Negeri Semarang.

      [12] Suhartono and Subanar. 2006. The Optimal Determination of Space Weight in GSTAR Model by Using Cross-sorrelation Inference Journal of Quantitative Methods: Journal Devoted the Mathematical and Statistical Application in Various Field.

      [13] Setiawan. 1992. Study on Seemingly Unrelated Regression (SUR) and its application to Almost Ideal Demand System (AIDS). Thesis Unpublished. Bogor : IPB.

      [14] Srivastava, V.K. and Giles, D.E.A. (1987). Seemingly unrelated regression equations models: estimation and inference. New York: Marcel Dekker. ISBN 978-0-82-477610-7.

      [15] Wutsqa, D.U dan Suhartono. 2010. Peramalan Deret Waktu Multivariat Seasonal pada Data Pariwisata dengan VAR-GSTAR. Jurnal Ilmu Dasar Vol. II no. 1.

      [16] Zellner, A. (1962). An efficient method of estimating seemingly unrelated regression equations and tests for aggregation bias. Journal of the American Statistical Association 57: 348–368.

  • Downloads

  • How to Cite

    Iriany, A., Firdaus Mahmudi, W., Hadi Nugroho, W., & Dwi Sulistyono, A. (2019). Rainfall Forecasting Using Gstar-Sur-Nn Approach in West Java Province. International Journal of Engineering & Technology, 8(1.9), 161-165. https://doi.org/10.14419/ijet.v8i1.9.26392