Analysis and Forecasting of Unilever Indonesia Stock Prices Using a Long Short-Term Memory (LSTM) Model
Keywords:
Long Short-Term Memory, stock price forecasting, machine learning, Recurrent Neural Network, MAPEAbstract
This study aims to forecast the stock price of Unilever Indonesia using the Long Short-Term Memory (LSTM) model. The dataset consists of weekly stock price data from May 2015 to May 2025, representing a financial time series with nonlinear and dynamic patterns. The LSTM model is employed due to its capability to capture long-term dependencies in sequential data. To evaluate model performance, the dataset is partitioned into three training–testing scenarios, namely 90:10, 80:20, and 70:30. Model accuracy is assessed using the Mean Absolute Percentage Error (MAPE). The results indicate that the best predictive performance is achieved using the 90:10 data split, yielding the lowest MAPE value of 6.973%, which falls into the highly accurate forecasting category. In comparison, the 70:30, and 80:20 scenarios produce higher MAPE values of 13.732% and 17.263% respectively. These findings demonstrate that increasing the proportion of training data significantly improves the performance of the LSTM model in forecasting stock prices. This study highlights the effectiveness of LSTM in modeling financial time series and provides practical insights for data-driven decision-making in stock market analysis.
References
Ahmar, A. S., & del Val, E. B. (2020). SutteARIMA: short-term forecasting method, a case: Covid-19 and stock market in Spain. Science of The Total Environment, 138883. doi: 10.1016/j.scitotenv.2020. 138883. DOI: https://doi.org/10.1016/j.scitotenv.2020.138883
Baek, Y., & Kim, H. Y. (2021). ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module. Expert Systems with Applications, 177, 114981. https://doi.org/10.1016/j.eswa.2021.114981
Bianchi, F. M., Scardapane, S., & Uncini, A. (2021). Time-series prediction with deep learning: A survey. Neural Networks, 139, 1–17. https://doi.org/10.1016/j.neunet.2021.02.006
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley. https://doi.org/10.1002/9781118675021
Chollet, F. (2021). Deep Learning with Python (2nd ed.). Manning Publications.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486
Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O’Reilly.
Jiang, W. (2022). Applications of deep learning in stock market prediction: Recent progress. IEEE Access, 10, 123456–123470. https://doi.org/10.1109/ACCESS.2022.3145678
Kim, T., & Won, C. (2022). Forecasting the stock market index using LSTM. Expert Systems with Applications, 197, 116697. https://doi.org/10.1016/j.eswa.2022.116697
Li, X., Xie, H., Wang, R., Cai, Y., Cao, J., Wang, F., & Min, H. (2021). Empirical analysis: Stock market prediction via extreme learning machine. Neural Computing and Applications, 33, 1–15. https://doi.org/10.1007/s00521-020-05535-1
Livieris, I. E., Pintelas, E., & Pintelas, P. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural Computing and Applications, 32, 17351–17360. https://doi.org/10.1007/s00521-020-04867-2
Meliyana, S. M., Aidid, M. K., & Rahmadhani, A. (2025). Implementation of Support Vector Regression (SVR) and Double Exponential Smoothing (DES) for Forecasting BRI Stock Prices . ARRUS Journal of Mathematics and Applied Science, 5(2), 42–53. https://doi.org/10.35877/mathscience4282
Nelson, D. M. Q., Pereira, A. C. M., & de Oliveira, R. A. (2017). Stock market’s price movement prediction with LSTM neural networks. International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN.2017.7966019
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2023). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 213, 118901. https://doi.org/10.1016/j.eswa.2022.118901
Shen, G., Tan, Q., Zhang, H., Zeng, P., & Xu, J. (2020). Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Computer Science, 131, 895–903. https://doi.org/10.1016/j.procs.2018.04.298
Singh, P. K., Chouhan, A., Bhatt, R. K., Kiran, R., & Ahmar, A. S. (2022). Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA. Quality & quantity, 56(4), 2023–2033. https://doi.org/10.1007/s11135-021-01207-6
Zhang, Y., Aggarwal, C., & Qi, G. (2020). Stock price prediction via discovering multi-frequency trading patterns. Proceedings of KDD. https://doi.org/10.1145/3394486.3403176
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Sitti Masyitah Meliyana, Abdul Rahman
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
