Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

Random Forest Lag Distributed Regression for Forecasting on Palm Oil Production

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290493,
        author={Aulia Rizki  Firdawanti and I Made  Sumertajaya and Bagus  Sartono},
        title={Random Forest Lag Distributed Regression for Forecasting on Palm Oil Production},
        proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia},
        publisher={EAI},
        proceedings_a={ICSA},
        year={2020},
        month={1},
        keywords={lag distributed regression palm oil production random forest regression},
        doi={10.4108/eai.2-8-2019.2290493}
    }
    
  • Aulia Rizki Firdawanti
    I Made Sumertajaya
    Bagus Sartono
    Year: 2020
    Random Forest Lag Distributed Regression for Forecasting on Palm Oil Production
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290493
Aulia Rizki Firdawanti1,*, I Made Sumertajaya1, Bagus Sartono1
  • 1: Department of Statistics, Bogor Agricultural University, Bogor, 16680, Indonesia
*Contact email: arfirdawanti@gmail.com

Abstract

Palm oil is one of the most cultivated potential commodities so it is necessary to do research to determine the determinants of production and forecasting on palm oil production. The objectives are perform data modeling dan forecasting using random forest lag distributed regression on palm oil production. This analysis combines the lag distributed regression and random forest methods. The results showed that the performances for this model are the correlation value is 0.9302, RMSE is 20.379, MAE is 14.143, and R-Square is 0.829. The 5 most important variables were quantity of palm oil, land area, palm oil age, 8th lag of wind velocity, and 1st lag of temperature. The distribution of data forecasting results are not much different from the distribution of testing data and original data.