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Vol.10, No.1, February 2021. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Estimation of Gourami Supplies Using Gradient Boosting Decision Tree Method of XGBoost
I Made Sukarsa, Ngakan Nyoman Pandika Pinata, Ni Kadek Dwi Rusjayanthi, Ni Wayan Wisswani
© 2021 I Made Sukarsa, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 10, Issue 1, Pages 144-151, ISSN 2217-8309, DOI: 10.18421/TEM101-17, February 2021.
Received: 18 August 2020. Revised: 16 January 2021.
Abstract:
The need for food supplies are very crucial in a food business, therefore it is necessary to estimate the right supplies to maximize profit. One of the methods to determine these is by looking for patterns and forecasting transaction data. The purpose of this research is to estimate the gourami supplies using transaction data to forecast using the gradient boosting decision tree method from XGBoost. The transaction data used comes from Restaurant X with a time period from 2016 to 2019. A measurement error rate of the model is achieved by using MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This study tried five XGBoost models with different features such as lag, rolling window, mean encoding, and mix. The results of this study indicate that the mixed feature model produces an accuracy of 97.54% with an MAE of 0.63 and a MAPE of 2.64%.
Keywords: Forecasting, Time Series, GBDT, XGBoost, Gourami Inventory. |
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