Implementasi Algoritma Regresi Linear untuk Memprediksi Harga Emas

Abstract

This study aims to predict gold prices using several independent variables, including the silver exchange rate (SLV), the S&P 500 index (SPX), the United States Oil Fund (USO) stock exchange rate, and  the  Euro  (EUR)  to  United  States  dollar  (USD)  exchange  rate.  The  data  used  in  this  study  is secondary  data  sourced  from  "Gold  Price  Data,"  comprising  a  total  of  2290  observations  and  7 columns. The method employed is regression, which is a technique for building predictive models based on  given  input  values.  The  prediction  results  are  evaluated  based  on  the  Root  Mean  Square  Error (RMSE)  value,  where  a  smaller  RMSE  indicates  better  accuracy.  The  study's  results  show  that  the single-variable model has an accuracy of 73%, while the multi-variable model has an accuracy of 84%.To  improve  prediction  accuracy,  this  study  recommends  using  alternative  predictive  models  and improving  the  dataset  division  to  ensure  a  more  representative  distribution.  This  research  not  only contributes to gold price prediction but also to the development of more accurate predictive models by utilizing relevant economic variables.