Implementation of Artificial Neural Network in Predicting CPO Prices Using Backpropagation

  • Muhmmad Rofi Halmi Information System Department, Universitas Islam Negeri Sumatera Utara
  • Raissa Amanda Putri Information System Department, Universitas Islam Negeri Sumatera Utara
https://doi.org/10.25139/inform.v9i2.8434
Abstract views: 193 , PDF downloads: 133
Keywords: Machine Learning, CPO Prices, Artificial Neural Network, Backpropagation, Prediction, Regression, Price Fluctuation

Abstract

This research examines the development of a forecasting model for Crude Palm Oil (CPO) prices using artificial neural network algorithms, particularly the backpropagation algorithm. CPO, as Indonesia's main export commodity, has significant economic impacts and affects the income of oil palm farmers. Data on CPO prices taken from CIF Rotterdam from January 2019 to December 2023 were used in this study. The research method involved several stages, including data collection, pre-processing, model design, and model implementation using Python programming. The results of training the model using the backpropagation algorithm showed an error value of 0.537829578 after 1000 epochs, while evaluation using Mean Squared Error (MSE) showed an MSE value of 0.022709 during the training process and 0.017604 during the testing process. The model also produced predictions for CPO prices in the next three months: 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model can predict future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

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Published
2024-07-29
How to Cite
Rofi Halmi, M., & Amanda Putri, R. (2024). Implementation of Artificial Neural Network in Predicting CPO Prices Using Backpropagation. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 9(2), 161-165. https://doi.org/10.25139/inform.v9i2.8434
Issue
Vol. 9 No. 2 (2024)
Section
Articles

Copyright (c) 2024 Muhmmad Rofi Halmi, Raissa Amanda Putri

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