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Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province | Silalahi | JTAM (Jurnal Teori dan Aplikasi Matematika)

Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province

Desri Kristina Silalahi

Abstract


The government continues to carry out poverty reduction strategies in Indonesia, especially in West Java Province. West Java Province is a province that has the most populous population in Indonesia. This will affect the level of welfare and the amount of poverty. The strategy undertaken is inseparable from accurate poverty data and is available from year to year. Even from the available data, the government can forecast the number of poor people in the coming years. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is one of forecasting methods. SARIMA is the development of the ARIMA model which has a seasonal effect. Based on the results of the study, that poverty data forecasting in the province of West Java using the SARIMA method obtained SARIMA model (0,1,1) (1,1,1)4. This model is the best model for forecasting data with an R-Squared value of 98%, Mean Square Error is 7.705.5800.000 and Mean Absolute Percentage Error IS 2,81%. It’s means this SARIMA model is very good in predicting poverty data in West Java Province.

Keywords


Forecasting; Poverty Data; SARIMA Method; Government Policy.

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References


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DOI: https://doi.org/10.31764/jtam.v4i1.1888

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