Modeling ARIMA Method and Support Vector Regression (SVR) at PT Telkom Indonesia Stock Price

Forecasting is an activity or process of predicting future values based on past data. In this study the data to be used is the stock price data of PT Telkom Indonesia. Forecasting that is commonly used in time series data is the ARIMA method, but there is a more practical method because it does not require the assumption of stationary data, namely the Support Vector Regression (SVR) method. Although the SVR method has the advantage of not requiring stationary data assumptions, the SVR method also has a weakness, namely it is not good at time series data. The results of forecasting PT Telkom stock price using ARIMA has a model with a MAPE of 2% while using SVR has a model with a MAPE of 3% this indicates that forecasting using the ARIMA method is better than the SVR method