Classical regression analysis is a statistical technique for modeling, forecasting and investigating the relationship between
response variable and explanatory variables. However, there are model adequacy must be checked on residual model i.e.
autocorrelation. The autocorrelation problem can be solved by modeling the residual of regression model into model that specifically
incorporates the autocorrelation structure. Autocorrelation can be caused by residual of regression model increasing over time. The
time series regression model is one of the analyzes used to accommodate the model residual which increasing over time. This study
used data on the broad proportion of rice blast (Pyricularia grisea) attacks. The purpose of this study is to forecast the broad proportion
of rice blast attacks used classical regression model and time series regression model. Evaluate forecast values used mean absolute
percentage error (MAPE). The comparison results showed that the forecast of time series regression model better than classical
regression model.
Published In:IJCSN Journal Volume 6, Issue 6
Date of Publication : December 2017
Pages : 766-770
Figures :03
Tables : 04
I. Setiawan : master student in Department of Statistics, Bogor
Agricultural University. His main interests is on Statistical Modelling.
I.M. Sumertajaya : Currently worked as a lecture in Department of
Statistics, Bogor Agricultural University. His main interests is on
Statistical Modelling Design of Experimental and Sampling
Methodology.
F.M. Afendi : Currently worked as a lecture in
Department of Statistics, Bogor Agricultural University. His main
interests is on Geoinformatics.
forecasting, MAPE, pyricularia grisea, regression, time series regression model
Forecasting using the time series regression model is
better than the classical regression model with the average
percentage of forecast error compared to the actual value
are 24.533%. The results of the forecast obtained not only
serve as an indicator of the arrival of blast disease
population causing attacks on rice commodities but the
forecast can also explain the actual value of broad
proportion of rice blast attack.
[1] 1. M. S. Sinaga, Dasar-dasar ilmu penyakit tumbuhan,
Jakarta, Indonesia : Penebar swadaya, 2003.
[2] Trisnaningsih, A. Nasution, “Respons ketahanan
berbagai galur padi rawa terhadap wereng coklat,
penyakit blas dan hawar daun bakteri”, Biodiversitas,
Vol. 2, 2016, pp. 85-92.
[3] Sudir, A. Nasution, Santoso, B. Nutryanto, Penyakit
Blas Pyricularia grisea pada tanaman padi dan strategi
pengendaliannya. Jakarta, Indonesia : Buletin Iptek
Tanaman Pangan KEMENTAN, 2014.
[4] D. C. Montgomery, L. C. Jennings and M. Kulahci,
Introduction to Time series Analysis and Forecasting.
New Jersey, US : John Wiley & Sons Inc, 2008.
[5] H.K Michael, J. N. Christopher, N. John, W. Li, Applied
Linear Statistical Models Fifth Edition, NewYork, US:
McGraw-Hill, 2005.
[6] B. McCune, J. B. Grace, Analysis of Ecological
Communities, Oregon, US : MjM Software Design, 2002.