Comparative Study of Garuda Indonesia Stock Price Prediction Using SVM, LSTM and Multiple Linear Regression

Abstract

Stock shares are one of the investment products or tools that have been used by many people. Shares have interesting options for saving or investment and are able to provide attractive returns based on corporate or company growth. Many factors can affect the share prices, whether internal or external companies. This research was conducted on Garuda Indonesia's stock price with the shares code (GIAA); Garuda Indonesia is one of the big airlines in Indonesia. Machine learning and deep learning are popular topics that give insight and recommendations for stock price movement and prediction. In this study, the researcher will compare the multiple linear regression, support vector machine, and long short-term memory model to give new insight to other researchers and investors using stock price data and exchange rate between IDR and USD data for a better decision in stock investment strategies. The results show that multiple linear regression gave the best result in predicting the stock price movement of Garuda Indonesia company with exchange rate currency between IDR and USD, with the best value result of R-Squared, MAPE, MSE, and RMSE. Showing that the exchange rate between IDR and USD is influenced by stock price movement