Samuel Valentino Hutagalung, Yennimar Yennimar, Erikson Roni Rumapea, Michael Justin Gesitera Hia, Terkelin Sembiring, Dhanny Rukmana Manday
{"title":"COMPARISON OF SUPPORT VECTOR REGRESSION AND RANDOM FOREST REGRESSION ALGORITHMS ON GOLD PRICE PREDICTIONS","authors":"Samuel Valentino Hutagalung, Yennimar Yennimar, Erikson Roni Rumapea, Michael Justin Gesitera Hia, Terkelin Sembiring, Dhanny Rukmana Manday","doi":"10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4125","DOIUrl":null,"url":null,"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.","PeriodicalId":499639,"journal":{"name":"Jusikom : Jurnal Sistem Informasi Ilmu Komputer","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jusikom : Jurnal Sistem Informasi Ilmu Komputer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.