Aplikasi Machine Learning dalam Prediksi Harga Saham Jakarta Islamic Index (JII) Menggunakan Metode Support Vector Regression

Faiza, Lala Nur
Agustina, Dina

Journal of Mathematics UNP
Vol 8, No 3 (2023): Journal Of Mathematics UNP

Investment is the process of allocating money into an asset with the goal of generating future profits. Investing in stocks generates higher returns but has a high risk as well. Therefore, the fluctuations in stock prices are very important so that investors need to analyze stock prices when making an investment. This study aims to apply machine learning in stock price prediction using the Support Vector Regression (SVR) method. JII stock data for the months of December 2021 to November 2022 were used in this study. Predicting stock prices is one of the processes in this research's data analysis processusing the Support Vector Regression (SVR) and then using RMSE to evaluate the model. The conclusions of this study indicate that SVR can be implemented as a method for predicting stock prices with the smallestRMSE values for ANTM, BRIS, and BRPT shares of0.0004.