Application of the Hybrid ARIMA-ANN Method to Predict the Stock Price of PT Telekomunikasi Indonesia (TLKM)

Abstract

Stock prices fluctuate over time, making them difficult to predict accurately. One commonly used method is the Autoregressive Integrated Moving Average (ARIMA), but it has limitations in capturing nonlinear patterns. To overcome this, an Artificial Neural Network (ANN) was used to create a hybrid ARIMA–ANN model. This study aims to determine the best model and compare the accuracy between the ARIMA model and the hybrid model in predicting the stock price of PT Telekomunikasi Indonesia (Persero) Tbk (TLKM). The data used are daily stock closing price data for the period June 2020 to June 2025. The best ARIMA model obtained is ARIMA (2,1,3), then the residuals are modeled using ANN with the (1–3–1) architecture. The results show that the ARIMA model produces a MAPE of 1.3179% and an RMSE of 60.52, while the hybrid ARIMA–ANN model produces a MAPE of 1.2941% and an RMSE of 59.01. Thus, the hybrid model is more effective in improving stock price prediction performance.