Implementation Of Autoregressive Intregated Moving Average (Arima) Methods For Forecasting Many Applicants Making Driver’s License A With Eviews 7 In Pati Indonesia
1WARDONO, - and SCOLASTIKA MARIANI, FMIPA Matematika and YULIYANA FATHONAH, - (2017) Implementation Of Autoregressive Intregated Moving Average (Arima) Methods For Forecasting Many Applicants Making Driver’s License A With Eviews 7 In Pati Indonesia. Journal of Theoretical and Applied Information Technology, 95 (10). ISSN 1992-8645
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Abstract
Driver’s License A or Surat Ijin Mengemudi A (SIM A) is the evidence given by the police to a person who has fulfilled all requirement of driving a motor vehicle. Data SIM A services than at past time can be used to predict the data in the future. One of them using Autoregressive Integrated Moving Average (ARIMA) methods with Eviews 7. The purpose of this research is to find the best model of ARIMA and using the best model to predict the average public services in the field of SIM A in Pati Regency, Indonesia for the coming period. The data used in the form of monthly data from January 2010 until December 2015. The steps in the search for the best model of ARIMA that are : stasionary test of the data using a data plot, a correlogram, and a unit root test; make the data become stationary by differencing and transformation logarithms; estimate the model when the data is already stationary; doing the diagnostic checking with a residual normality test, a autocorrelation test, and a heteroskedastic test; as well as selecting and determining the best model. The step resulted in the best model that is ARIMA (0,2,2) with a logarithmic transformation which has the value SSR = 0.937246, AIC = 0.002447, and R 2 = 0.755068.
Item Type: | Article |
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Uncontrolled Keywords: | Driver’s License A, Forecasting, ARIMA, Eviews 7 |
Subjects: | Q Science > QA Mathematics |
Fakultas: | Fakultas Matematika dan Ilmu Pengetahuan Alam > Matematika, S1 |
Depositing User: | mahargjo hapsoro adi |
Date Deposited: | 06 Apr 2022 04:51 |
Last Modified: | 06 Apr 2022 04:51 |
URI: | http://lib.unnes.ac.id/id/eprint/49526 |
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