Penerapan Extreme Learning Machine Dalam Meramalkan Harga Minyak Sawit Mentah

Siti Aisyah, Nurissaidah Ulinnuha, Abdulloh Hamid
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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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极限学习机在原油棕榈油价格预测中的应用
由于世界各地对植物油的大量需求,对粗棕榈油的需求有所增加。从2022年3月开始,粗棕榈油价格创下历史新高,导致国际食用油价格飙升,尤其是印尼。本研究的目的是通过测试参数,即隐藏神经元和激活函数来预测粗棕榈油的价格。采用极限学习机(ELM)方法。该方法是人工神经网络(ANN)方法的发展,可以克服学习速度过程中的缺点。本研究分为几个阶段:(1)对数据进行归一化预处理,采用时间序列分割法对数据进行分割;(2)采用ELM方法对数据进行分析,对参数进行测试,即隐藏神经元和激活函数;(3)对最佳参数试验结果进行分析;(4)对获得的最佳参数进行预测数据计算;本研究使用了从投资网站获得的2021年4月1日至2022年4月14日的毛棕榈油价格每日数据。已开展的研究结果得到MAPE和RMSE值分别为0.0173和0.0308,最佳参数为隐藏神经元数为5,二值s型激活函数。根据所获得的结果,希望这将使政府在未来更容易确定粗棕榈油的价格。
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