Penerapan Extreme Learning Machine Dalam Meramalkan Harga Minyak Sawit Mentah

Authors

  • Siti Aisyah Universitas Islam Negeri Sunan Ampel Surabaya
  • Nurissaidah Ulinnuha
  • Abdulloh Hamid

DOI:

https://doi.org/10.15575/kubik.v7i2.20460

Keywords:

Crude Palm Oil, Extreme Learning Machine, Time Series Split

Abstract

The need for crude palm oil has increased due to the large demand for vegetable oils in various parts of the world. Beginning in March 2022, the price of crude palm oil set a record high which caused international cooking oil prices to soar, especially for Indonesia. This study aims to predict the price of crude palm oil with test parameters, namely hidden neurons and activation functions. The method used is Extreme Learning Machine (ELM). This method is a development of the artificial neural network (ANN) method which can overcome weaknesses in the learning speed process. There are several stages in this study: (1) pre-processing the data by normalizing the data and dividing the data using the time series split method, (2) analyzing the data using the ELM method by testing parameters, namely hidden neurons and activation functions, (3) analyzing the results of the best parameter trials, (4) calculating forecasting data using the best parameters that have been obtained, and (5) analyzing the forecasting results that have been obtained. This study uses daily data on the price of crude palm oil from April 1 2021 to April 14 2022 obtained from the Investing website. The results of the research that has been carried out obtained MAPE and RMSE values of 0.0173 and 0.0308 with the best parameters namely the number of hidden neurons of 5 and the binary sigmoid activation function. Based on the results obtained, it is hoped that it will make it easier for the government to determine the price of crude palm oil in the future.

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Published

2023-06-05

Issue

Vol. 7 No. 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika

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