time series analysis/analisis runtut waktu

1. ABSTRACT
Baiq Diah Fitasari, 2011.-- DECOMPOSITION METHODS FOR FORECASTING
THE TUNGRO VIRAL ATTACKS ON RICE PLANTS IN WEST NUSA TENGGARA
PROVINCE (i – xiv; 94 pages) (Supervisors : I Gde Ekaputra Gunartha and I
Gede Adhitya Wisnu Wardhana)
Tungro is a viral disease of rice plants that commonly occurs in the vegetative
growth phase and causes plants to grow stunted and reduced the number of tillers.
Tungro attacks occurs from time to time due to the intensive manipulation of rice
growing environment in fulfilling human food needs due to the changes of macro and
micro climate of plants that match eco-biology of rice green planthopper. This
requires a modeling effort to forecast the attack of tungro in order to plan better rice
cultivation in the next growing seasons. The purpose of this study is to analyze the
use of decomposition methods to forecast the added area and intensity of tungro
attacks, as well as rice green planthopper population on rice plants in the Province
of.NTB. The obtained forecasting models, then were used to predict the added area
and intensity of tungro attacks, as well as rice green planthopper population in
growing seasons of 2010. To find a suitable forecasting model was used data of
period 2003 - 2009, whereas data of 2010 were used to validate the forecasts. Steps
used in this study include: (a). identification of the model by plotting the data, (b).
estimating the trend equations by using the method of least squares, (c). determining
the seasonal indices using the average percentage methods, (d). determining the
cyclical indices using 11 models, (e). forecasting by using the best obtained models
(choosing the smallest MSE value), and (f). comparing the forecast results with
actual data in rice planting seasons of 2010 using 95% confidence interval.
The findings show the best forecasting models for added area of tungro viral attacks
in the two districts (West Lombok and Dompu) include the components of trend, the
seasonal indices and the cyclical indices; while added area of tungro attacks that
recorded in other districts (Central Lombok, East Lombok, Sumbawa and Bima),
the intensity of tungro viral attacks and rice green planthopper population forecasting
models are fitted using only the value components of the trend and seasonal indices
(the smallest MSE value). The selected models are then compared with the actual
data of rice-planting seasons in 2010. All models are valid, because the forecasts
and the actual data are both spread out between the lower boundary and the upper
boundary lines of the 95% confidence interval. This means that the decomposition
models found in this study to forecast the added area and the attack intensity of
tungro virus, as well as rice green planthopper population in the Province of NTB fit
with the actual data distribution patterns.
Key words : forecasting, decomposition method, moving average, tungro virus,
green planthopper.
©
Program Studi Matematika – FMIPA Universitas Mataram (2011)