Research Article
Random Forest Lag Distributed Regression for Forecasting on Palm Oil Production
@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
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.