IMPLEMENTASI DATA MINING DALAM PREDIKSI HARGA SAHAM BBNI DENGAN PEMODELAN MATEMATIKA MENGGUNAKAN METODE LSTM DENGAN OPTIMASI ADAM

Alrafiful Rahman, Lucia Sri Istiyowati, Valentinus Valentinus, Ivan Ivan, Zainal Azis

Abstract


Stock price prediction plays a crucial role in investment decision-making, allowing investors to maximize profits and minimize risks. This study implements the Long Short-Term Memory (LSTM) method with Adam optimization to predict the stock price of Bank Negara Indonesia (BBNI) based on historical stock price data from the Indonesia Stock Exchange (2001-2023). LSTM is chosen for its ability to handle sequential data and identify long-term patterns in time series. Meanwhile, the Adam optimization algorithm is used to accelerate model convergence and improve prediction accuracy. The data used includes daily stock prices (closing prices), and the research process involves data collection, preprocessing, LSTM model creation, Adam optimization, training, evaluation, and prediction. The experimental results show that the model with a batch size of 64 and 100 epochs yields an R² of 0.9928 and a MAPE of 1.53%, indicating a very high prediction accuracy. With an accuracy of 98.46%, the LSTM model with Adam optimization proves to be effective in predicting stock prices, providing excellent results for applications in investment strategies. This study demonstrates the great potential of applying data mining and machine learning techniques in more informed and data-driven stock market analysis.


Keywords


Stock Price Prediction, Long Short-Term Memory (LSTM), Adam Optimization, Data Mining

Full Text:

PDF

References


Arkadia, B., Hananto, B., & Prasvita, D. S. (2022). Optimasi long short-term memory dengan Adam menggunakan data udara kota DKI Jakarta. In Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA) (pp. 92–101).

Bao, W., Cao, Y., Yang, Y., Che, H., Huang, J., & Wen, S. (2024). Data-driven stock forecasting models based on neural networks: A review. Information Fusion, 113, Article 102616. https://doi.org/10.1016/j.inffus.2024.102616

Dineshkumar, P., & Subramani, D. B. (2024). Foresight in finance: Elevating predictions with enhanced RNN-LSTM and Adam optimizer. Educational Administration: Theory and Practice, 30(5), 13637–13646. https://doi.org/10.53555/kuey.v30i5.5931

Emanuella, T., Lawi, A., & Hendra. (2022). Model prediksi harga saham Apple Inc pada beberapa bursa efek menggunakan metode multivariate gated recurrent unit. In Prosiding Seminar Nasional Teknik Elektro dan Informatika (SNTEI) (pp. 298–303).

Faridah, N., & Sugiantoro, B. (2023). Analisis optimasi pada algoritma long short-term memory untuk memprediksi harga saham. Jurnal Media Informasi Budidarma, 7(1), 575. https://doi.org/10.30865/mib.v7i1.5421

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

Gudelek, M. U., Boluk, S. A., & Ozbayoglu, A. M. (2017). A deep learning based stock trading model with 2-D CNN trend detection. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1–8). https://doi.org/10.1109/SSCI.2017.8285188

Gunawan, E. K., & Wibowo, A. (2023). Stock price movement classification using ensembled model of long short-term memory (LSTM) and random forest (RF). JOIV: International Journal of Informatics and Visualization, 7(4), 2255. https://doi.org/10.30630/joiv.7.4.01640

Indonesia Stock Exchange. (2024, November 13). Stock index. Retrieved from https://www.idx.co.id/en/market-data/stocks-data/stock-index/

Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (pp. 1–15).

Nabillah, & Ranggadara, I. (2020). Mean absolute percentage error untuk evaluasi hasil prediksi komoditas laut. JOINS: Journal of Information Systems, 5(2), 250–255. https://doi.org/10.33633/joins.v5i2.3900

Pipin, S. J., Purba, R., & Kurniawan, H. (2023). Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) Dengan Optimasi Adaptive Moment Estimation. Journal of Computational System Informatics, 4(4), 806–815. https://doi.org/10.47065/josyc.v4i4.4014

Puteri, D. I. (2023). Implementasi long short-term memory (LSTM) dan bidirectional long short-term memory (BiLSTM) dalam prediksi harga saham syariah. Euler: Jurnal Ilmiah Matematika, Sains, dan Teknologi, 11(1), 35–43. https://doi.org/10.34312/euler.v11i1.19791

Rosyd, A., Purnamasari, I., & Ali, I. (2024). Penerapan metode long short-term memory (LSTM) dalam memprediksi harga saham PT Bank Central Asia. JATI: Jurnal Mahasiswa Teknik Informatika, 8(1), 501–506. https://doi.org/10.36040/jati.v8i1.8440

Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2018). A comparison of ARIMA and LSTM in forecasting time series. In Proceedings - 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1394–1401). https://doi.org/10.1109/ICMLA.2018.00227

Siswanto, J., Manongga, D., Sembiring, I., & Wijono, S. (2024). Deep learning based LSTM model for predicting the number of passengers for public transport bus operators. Journal of Online Informatics, 9(1), 18–28. https://doi.org/10.15575/join.v9i1.1245

Wathani, M. N., Kusrini, K., & Kusnawi, K. (2023). Prediksi tren pergerakan harga saham PT Bank Central Asia Tbk, dengan menggunakan algoritma long short-term memory (LSTM). Infotek: Jurnal Informatika dan Teknologi, 6(2), 502–512. https://doi.org/10.29408/jit.v6i2.19824

Zhao, Y. (2023). Stock prediction based on LSTM neural network. Highlights in Business, Economics and Management, 3, 19–23.




DOI: https://doi.org/10.31932/jutech.v5i2.4137

Article Metrics

Abstract view : 555 times
PDF - 275 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JUTECH : Journal Education and Technology



Creative Commons License
JUTECH: Journal Education and Technology is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.