PREDICTION OF BIODIESEL FUEL PRICES USING MULTIPLE LINEAR REGRESSION ALGORITHMS

  • Deny Haryadi (1*) Universitas Telkom
  • Dewi Marini Umi Atmaja (2) Universitas Medika Suherman
  • Adi Kuncoro (3) Universitas Telkom

  • (*) Corresponding Author
Keywords: biodiesel price, data mining, linear regression algorithm, prediction

Abstract

Biodiesel is a fuel derived from palm oil and a type of fuel that is an alternative to renewable energy, can be renewed and has the potential to become a substitute for fossil sources that are used non-stop. The utilize of biodiesel can be an arrangement for Indonesia to diminish reliance on imported diesel fuel since biodiesel does not contain sulfur and is demonstrated to be ecologically inviting. The price of biodiesel-type biofuels can increase, decrease, or remain constant due to factors that influence it, including the price of biodiesel competitors, palm oil, and world crude oil. For this reason, it is necessary to have a method that can predict the price of biodiesel-type fuel so that in the future, the price of biodiesel-type biofuel does not decrease or become unable to compete with its competitors. Prediction of biodiesel fuel prices can be done by implementing a multiple linear regression algorithm, one of the data mining algorithms. RMSE results obtained in this study were 0.003 with a standard deviation of +/- 0.000 so it can be concluded that this algorithm is quite accurate in predicting the price of biodiesel-type biofuels. A comparison of the results of manual calculations with the implementation of RapidMiner in the study obtained the same results because there was a causal relationship between attributes. The use of the multiple linear regression algorithm in this research is useful in planning the right strategy and making decisions to maintain biodiesel market price stability in the future.

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Published
2024-02-01
How to Cite
[1]
D. Haryadi, D. Umi Atmaja, and A. Kuncoro, “PREDICTION OF BIODIESEL FUEL PRICES USING MULTIPLE LINEAR REGRESSION ALGORITHMS”, jitk, vol. 9, no. 2, pp. 180-187, Feb. 2024.
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