Forecasting the Number of Agricultural Households in Indonesia

M. Fajar¹ , N. Rahmadhani² , Suwandari ³

Section:Research Paper, Product Type: Journal-Paper
Vol.6 , Issue.12 , pp.10-17, Dec-2020

Online published on Dec 31, 2020

Copyright © M. Fajar, N. Rahmadhani, Suwandari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This study aims to forecast the number of agricultural households as the provision of information input for policymaking and the foundation for the implementation of agricultural census in the future. The data used in this study are the number of agricultural households from the 2003 and 2013 agricultural censuses that available only once in ten years, and the results of SUTAS2018 (Intercensal Agricultural Census). The method used in this study is the intercensal forecasting and the hybrid method. The intercensal forecasting of agricultural households by province and national aggregate. The hybrid method proposes three models, namely exponential smoothing state space (ESSS), autoregressive neural network (NNAR), and TBATS (Transformations Box-Cox, ARMA error, Trends, and Seasonal). Time series are input data for processing hybrid method, then produce forecasts. The results obtained from this study are the intercensal and Hybrid ENT can be applied to the limited data availability. The results of forecasting the number of agricultural households in the 2014 - 2023 period show a downward trend as the implications of historical data resulting from intercensal having a downward trend, and based on MAPE show that the hybrid ENT method is quite good in forecasting agricultural households. But in further research, auxiliary variables and non-sample information are needed so that the forecasting results become more accurate.

Key-Words / Index Term :
forecasting, household, agriculture, intercensal, hybrid ENT

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