COMPARISON OF SUPPORT VECTOR REGRESSION AND RANDOM FOREST REGRESSION ALGORITHMS ON GOLD PRICE PREDICTIONS

Authors

  • Samuel Valentino Hutagalung a:1:{s:5:"en_US";s:27:"Universitas Prima Indonesia";}
  • Yennimar Yennimar Universitas Prima Indonesia
  • Erikson Roni Rumapea Universitas Prima Indonesia
  • Michael Justin Gesitera Hia Universitas Prima Indonesia
  • Terkelin Sembiring Universitas Prima Indonesia
  • Dhanny Rukmana Manday Universitas Prima Indonesia

DOI:

https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4125

Abstract

This research was conducted to test how the Support Vector Regression and Random Forest Regression algorithms predict gold futures prices. The data used in this research was taken from the Investing.com website which will later be processed into a prediction model by comparing the SVR and RVR algorithms. The Support Vector Regression and Random Forest Regression algorithms will be tested to see the performance of each prediction model. The test results show that the Support Vector Regression model is superior in terms of accuracy with a value of 83%. However, the Random Forest Regression algorithm is superior with a smaller error rate, namely with an MSE value of 270.85 and an MAE value of 12.53.

Keyword: Comparison, Prediction, Support Vector Regression, Random Forest Regression.

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Published

2023-09-01

How to Cite

[1]
S. V. Hutagalung, Y. Yennimar, E. R. Rumapea, M. J. G. Hia, T. Sembiring, and D. R. Manday, “COMPARISON OF SUPPORT VECTOR REGRESSION AND RANDOM FOREST REGRESSION ALGORITHMS ON GOLD PRICE PREDICTIONS”, JUSIKOM PRIMA, vol. 7, no. 1, pp. 255-262, Sep. 2023.

Issue

Vol. 7 No. 1 (2023): Jurnal Sistem Informasi dan Ilmu Komputer Prima (JUSIKOM Prima)

Section

Articles

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Copyright (c) 2023 Samuel Valentino Hutagalung, Yennimar Yennimar, Erikson Roni Rumapea, Michael Justin Gesitera Hia, Terkelin Sembiring, Dhanny Rukmana Manday

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