Forecasting Next-Day TLKM Stock Prices Using Linear and Nonlinear Machine Learning Models: A Case Study of PT Telekomunikasi Indonesia Tbk

Edwin Setiawan Nugraha; Azizah Dwi Aprilia; Samsul Arifin; Norani Saro

doi:10.15408/jti.v19i2.50156

Authors

Edwin Setiawan Nugraha Department of Actuarial Science, President University, Indonesia
Azizah Dwi Aprilia Department of Actuarial Science, President University, Indonesia
Samsul Arifin Department of Data Science, Institut Teknologi Sains Bandung, Indonesia
Norani Saro Faculty of Humanities dan Education, UNITAR International University, Malaysia

DOI:

https://doi.org/10.15408/jti.v19i2.50156

Keywords:

closing price, linear regression, random forest, backpropagation, tlkm

Abstract

Stock price forecasting provides useful information for investors in making decisions in fluctuating capital markets. The objective of this work is to develop and compare models to forecast the closing price for the next trading day. Three models were employed: linear regression, an optimized random forest regression, and an optimized backpropagation neural network. The dataset used consists of daily market data for TLKM and IHSG covering the period from March 22, 2018, to February 24, 2027. The predictors used include open price, high price, low price, trading volume, SMA₅, and RSI₁₄, while the target variable is the next day's TLKM closing price. The dataset was divided chronologically into 80% training and 20% testing sets. Time-series cross-validation was performed using 5-fold TimeSeriesSplit. Hyperparameter optimization was applied to the random forest and backpropagation neural network models using RandomizedSearchCV. The results show that linear regression outperforms the other models based on the overall evaluation metrics, with an MAE of 57.67, RMSE of 81.38, MAPE of 1.37%, and of 92.8%. The MAPE value indicates that the linear regression model has a relatively low forecasting error. The linear regression model can also explain approximately 92.8% of the variation in the next day's TLKM closing price through the predictors included in the model. The implication of this work is that a linear regression model can provide an additional quantitative reference for investors in evaluating the possibility of TLKM closing price movements on the next trading day.

References

[1] Z. Bodie, A. Kane, and A. J. Marcus, Investments, 13th ed. New York, NY, USA: McGraw Hill, 2024. ISBN: 978-1-266-08596-3.

[2] M. M. Kumbure, C. Lohrmann, P. Luukka, and J. Porras, “Machine learning techniques and data for stock market forecasting: A literature review,” Expert Systems with Applications, vol. 197, Art. no. 116659, 2022, doi: 10.1016/j.eswa.2022.116659.

[3] I. Himawan, O. Nurdiawan, and G. Dwilestari, “Prediksi harga saham dengan algoritma regresi linier dengan RapidMiner,” JURSIMA, vol. 10, no. 3, 2022, doi: 10.47024/js.v10i3.475.

[4] D. Ambarita, R. G. Marpaung, and J. P. Nainggolan, “Analysis of closing price forecasts for PT Bank Mandiri (Persero) Tbk (BMRI) stock using the multiple linear regression method based on OHLCV data,” Journal of Management Analytical and Solution, vol. 6, no. 2, pp. 85–96, 2026, doi: 10.32734/jomas.v6i2.25006.

[5] M. Hasbi, M. Jamil, M. H. Akbar, and Sharipuddin, “Analisis dan prediksi harga saham menggunakan metode Random Forest,” Jurnal Ilmiah Media Sisfo, 2026.

[6] Y. H. Parulian, B. Subartini, and Riaman, “Analisis pengaruh IHSG, inflasi, BI Rate dan nilai tukar Rupiah terhadap fluktuasi harga saham bank di Indonesia menggunakan metode regresi linear berganda,” Jurnal Matematika Integratif, vol. 9, no. 1, pp. 19–28, 2013, doi: 10.24198/jmi.v9.n1.10195.19-28.

[7] E. Fitri and D. Riana, “Analisa perbandingan model prediction dalam prediksi harga saham menggunakan metode Linear Regression, Random Forest Regression dan Multilayer Perceptron,” METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi, vol. 6, no. 1, pp. 69–78, 2022, doi: 10.46880/jmika.Vol6No1.pp69-78.

[8] C. Fieberg, D. Metko, T. Poddig, and T. Loy, “Machine learning techniques for cross-sectional equity returns’ prediction,” OR Spectrum, vol. 45, no. 1, pp. 289–323, 2023, doi: 10.1007/s00291-022-00693-w.

[9] S. Suyatna, A. J. Satrio, S. P. Pratama, M. Indarto, F. Syarif, and F. Amsury, “Analisis perbandingan kinerja model machine learning dalam prediksi harga saham TLKM,” RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 4, pp. 829–837, 2025, doi: 10.31004/riggs.v4i4.3476.

[10] S. Lumbantoruan, A. Duha, T. P. Astuti, A. J. Wahidin, B. Rahmatullah, and I. Kurniawati, “Prediksi harga saham PT Telkom Indonesia Tbk (TLKM) menggunakan Linear Regression, Random Forest, dan XGBoost,” RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 4, pp. 3429–3438, 2025, doi: 10.31004/riggs.v4i4.4158.

[11] M. T. Pawitra, L. Abdurrahman, and H. Fakhrurroja, “FinBERT-based sentiment integration in hybrid CNN–BiLSTM models for stock price forecasting,” Jurnal Teknik Informatika, vol. 19, no. 1, pp. 73–84, 2026, doi: 10.15408/jti.v19i1.49466.

[12] A. Q. Md, S. Kapoor, C. J. Chris, A. K. Sivaraman, K. F. Tee, H. Sabireen, and N. Janakiraman, “Novel optimization approach for stock price forecasting using multi-layered sequential LSTM,” Applied Soft Computing, vol. 134, p. 109830, 2023, doi: 10.1016/j.asoc.2022.109830.

[13] C. Li and G. Qian, “Stock price prediction using a frequency decomposition based GRU Transformer neural network,” Applied Sciences, vol. 13, no. 1, p. 222, 2023, doi: 10.3390/app13010222.

[14] T. Li, Z. Liu, Y. Shen, X. Wang, H. Chen, and S. Huang, “MASTER: Market-Guided Stock Transformer for Stock Price Forecasting,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 1, pp. 162–170, 2024, doi: 10.1609/aaai.v38i1.27767.

[15] J. V. R. Ferro, R. J. R. Dos Santos, E. B. Costa, and J. R. S. Brito, “Machine learning techniques via ensemble approaches in stock exchange index prediction: Systematic review and bibliometric analysis,” Applied Soft Computing, vol. 167, p. 112359, 2024, doi: 10.1016/j.asoc.2024.112359.

[16] R. Chopra, G. D. Sharma, and V. Pereira, “Identifying bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction,” Technovation, vol. 135, p. 103067, 2024, doi: 10.1016/j.technovation.2024.103067.

[17] C. Bergmeir, R. J. Hyndman, and B. Koo, “A note on the validity of cross-validation for evaluating autoregressive time series prediction,” Computational Statistics & Data Analysis, vol. 120, pp. 70–83, 2018, doi: 10.1016/j.csda.2017.11.003.

[18] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York, NY, USA: Springer, 2009.

[19] G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, An Introduction to Statistical Learning: With Applications in Python. Cham, Switzerland: Springer, 2023.

[20] C. Lewis, Industrial and Business Forecasting Methods: A Radical Guide to Exponential Smoothing and Curve Fitting, vol. 56(2), London: Butterworth Scientific, 1982