Prediksi Harga Penutupan Saham Gojek-Tokopedia Menggunakan Model Hybrid GARCH-LSTM - Pramudito, Farhan Wegig; Arianto, Kezia Jazzlyn; Thoyib, Najma Humairoh; Arvintyani, Risquina Angelica; Herlambang, Yudhistira Jalu; Zuhdi, Shaifudin (Journal of Information System Research (JOSH), 2026)
This study proposes the application of a hybrid GARCH–LSTM model to predict GoTo stock prices in the context of Indonesia's rapidly growing digital economy. GoTo stock prices are characterized by high volatility and a non-linear time series pattern, making them difficult to model using conventional approaches. Daily closing price data from 2022 to November 2025 are transformed into logarithmic returns to meet the stationarity assumption. The GARCH(1,1) model is used to estimate conditional volatility, which represents short-term risk dynamics and the volatility clustering phenomenon. Furthermore, historical returns and conditional volatility are used as additional features in the LSTM model to predict the next period's stock returns, which are then converted back into closing price predictions. The estimation results show that all GARCH parameters are statistically significant, indicating the persistence of volatility in GoTo stock data. Evaluation of the performance of the hybrid model on the test data produces an RMSE value of 3.126, an MAE of 2.245, and a coefficient of determination (R²) of 0.899, indicating that the model is able to represent stock price movement patterns well. These findings indicate that the hybrid GARCH–LSTM approach is effective in modeling stock price dynamics under highly volatile market conditions.