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Modelling and Prediction of Rice Price in East Java using Approach
to the Multiplicative Time Series Analysis
Sediono and Satya Purnama
Department of Mathematics, Airlangga University, Surabaya, Indonesia 60115
Keywords: Rice Price, Bulog, Modelling, Prediction, Multiplicative Time Series, Arima.
Abstract: About 78% of Indonesia's population take a rice to meet daily carbohydrate intake. However, the fluctuating
of the rice price is one of the problems that should be faced by Bulog in East Java. Therefore, this research
aims to model and predict the rice price in East Java. We use the ARIMA Multiplicative Time Series
analysis to model and predict the rice price. The basis of the Multiplicative time series analysis is that the
factors affecting the pattern of the data set in the past and present tend to change little in the future. Thus,
the time series analysis can assist the researchers to make some decisions. The appropriate model for the
rice price data in East Java is ARIMA seasonal model. These are due to the harvest time. Based on the
smallest MSE, the result shows that the appropriate model for the rice price data in East Java from January
2008 to December 2016 is ARIMA(0,1,1)(0,1,1)12 or IMAISMA. There are no significant differences
between prediction price of rice in East Java for the period of January 2017 to August 2017 and sample
data.
1 INTRODUCTION
About 78% of Indonesia's population take a rice to
fulfill the carbohydrate intake (Prawira, 2013). Rice
becomes a very important food requirement for the
people of Indonesia and according to an article
released by International Rice Research Institute
(IRRI) in 2014 Indonesian’s rice consumption
reaches 125 Kilogram (Kg) per capita per year.
Therefore, the government established a logistics
agency called BULOG (Logistic Business Entity)
which plays an important role in regulating the
supply of rice, rice stock, minimum stock, rice price,
and others. East Java is one of the rice barns and
serves as a national food buffer. East Java is able to
supply more than 17 percent of the national rice and
supply rice in 15 other provinces through Bulog's
national distribution. Nationally, referring to Central
Bureau of Statistics (BPS) data, observed the
average price of rice in September 2014, grinding
rate for medium quality has increased price by 6.18
percent. Thus, the average medium price of rice at
the milling rate of 8,125.93 IDR increased by 1.45
percent. Central Bureau of Statistics (BPS) reported
inflation in September 2014 was quite low at 0.27
percent. Nevertheless, rice commodities returned to
be a contributing factor to inflation with a share of
0.02 percent. Previous research that discussed about
rice forecasting has been done is to predict the price
of rice in Perum BULOG East Java Division using
ARIMA method and double exponential smoothing.
Double Exponential Smoothing is used because the
data has a trend pattern but not seasonal. The results
show that interpretation of time series models is the
best method is ARIMA (Hartinungrum, 2012).
Therefore, in this study, we aim to make a model
forecasting rice price of milling in East Java using
time series analysis. The time series data is a set of
data in the form of numbers obtained within a
certain period of time. Time series data is usually in
the form of annual, semiannual, quarterly, monthly,
weekly, daily, and so on (Bisgaard & Kullahci,
2005; Wei, 2006). According to Santoso (2001) the
basis of time series data analysis is that the factors
that affect the pattern of the data set in the past and
now tend not to change much in the future. Thus, it
can be done time series data analysis to help
researchers in making decisions (Hartinungrum,
2012). Generally, the time series can be grouped into
two large chunks i.e. univariate and multivariate
time series, both seasonal and non-seasonal
(Santoso, 2001).
80
Sediono, . and Purnama, S.
Modelling and Prediction of Rice Price in East Java using Approach to the Multiplicative Time Series Analysis.
DOI: 10.5220/0008517500800084
In Proceedings of the International Conference on Mathematics and Islam (ICMIs 2018), pages 80-84
ISBN: 978-989-758-407-7
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