Pengembangan Analisis Teknikal Untuk Trading Bursa Saham dengan Long Short Term Memory

Authors

  • Faris Abdi El Hakim Politeknik Elektronika Negeri Surabaya, Surabaya
  • Arna Fariza Politeknik Elektronika Negeri Surabaya, Surabaya
  • Setiawardhana Setiawardhana Politeknik Elektronika Negeri Surabaya, Surabaya

DOI:

https://doi.org/10.30865/mib.v7i3.6410

Keywords:

Prediction, Stock Exchange, Technical Analysis, Deep Learning, Long Short Term Memory

Abstract

Stock price movements are difficult to predict and can change over time. Technical analysis is necessary to determine the right timing and company for stock investments. However, novice traders may face difficulties in analyzing stocks. Therefore, the author conducted research on the development of technical analysis for stock trading using Long Short-Term Memory (LSTM). The study utilized data from PT Bank Central Asia Tbk (BBCA.JK) stock prices, covering the period from July 1, 2004, to April 28, 2023. The LSTM method combined with technical analysis resulted in RMSE values of 136.759 for Open price, 126.52 for Close price, 317.968 for High price, 178.001 for Low price, and 189.669 for Adj. Close price, which outperformed the LSTM method without technical analysis. Furthermore, the LSTM method achieved better accuracy compared to Support Vector Regression (SVR) and K-Nearest Neighbors (KNN) using three different datasets. The RMSE values were 65.21 for LSTM, 313.56 for SVR, and 72.44 for KNN. The R2 values were 0.9919 for LSTM, 0.81 for SVR, and 0.990 for KNN. The results of the model were implemented in a web-based system using the Laravel framework and MySQL database.

References

M. Abdul Dwiyanto Suyudi, E. C. Djamal, A. Maspupah Jurusan Informatika, and F. Sains dan Informatika Universitas Jenderal Achmad Yani Cimahi, “Prediksi Harga Saham menggunakan Metode Recurrent Neural Network,†Semin. Nas. Apl. Teknol. Inf., pp. 1907–5022, 2019.

L. S. Hutasoit and F. Hutabarat, “Pengaruh Analisa Teknikal Terhadap Harga Sahambank Bumn Di Bursa Efek Indonesia Tahun 2020,†J. Ilm. Akunt. Dan Keuang., vol. 04, no. 01, pp. 27–39, 2021.

R. Pramudya and S. Ichsani, “Finance & Banking Studies Efficiency of Technical Analysis for the Stock Trading,†Pramudya Ichsani / Int. J. Financ. Bank. Stud., vol. 9, no. 1, p. 2020, 2020, [Online]. Available: www.ssbfnet.com/ojshttps://doi.org/10.20525/ijfbs.v9i1.666

M. Vijh, D. Chandola, V. A. Tikkiwal, and A. Kumar, “Stock Closing Price Prediction using Machine Learning Techniques,†Procedia Comput. Sci., vol. 167, no. 2019, pp. 599–606, 2020, doi: 10.1016/j.procs.2020.03.326.

Y. Wen, P. Lin, and X. Nie, “Research of stock price prediction based on PCA-LSTM model,†IOP Conf. Ser. Mater. Sci. Eng., vol. 790, no. 1, 2020, doi: 10.1088/1757-899X/790/1/012109.

G. Budiprasetyo, M. Hani’ah, and D. Z. Aflah, “Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM),†J. Nas. Teknol. dan Sist. Inf., vol. 8, no. 3, pp. 164–172, 2023, doi: 10.25077/teknosi.v8i3.2022.164-172.

J. Qiu, B. Wang, and C. Zhou, “Forecasting stock prices with long-short term memory neural network based on attention mechanism,†PLoS One, vol. 15, no. 1, pp. 1–15, 2020, doi: 10.1371/journal.pone.0227222.

J. Shen and M. O. Shafiq, “Short-term stock market price trend prediction using a comprehensive deep learning system,†J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00333-6.

C. Sang and M. Di Pierro, “Improving trading technical analysis with TensorFlow Long Short-Term Memory (LSTM) Neural Network,†J. Financ. Data Sci., vol. 5, no. 1, pp. 1–11, 2019, doi: 10.1016/j.jfds.2018.10.003.

X. Wang and C. Wang, “Time Series Data Cleaning: A Survey,†IEEE Access, vol. 8, pp. 1866–1881, 2020, doi: 10.1109/ACCESS.2019.2962152.

A. Moghar and M. Hamiche, “Stock Market Prediction Using LSTM Recurrent Neural Network,†Procedia Comput. Sci., vol. 170, pp. 1168–1173, 2020, doi: 10.1016/j.procs.2020.03.049.

A. Ntakaris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, Mid-price prediction based on machine learning methods with technical and quantitative indicators, vol. 15, no. 6 June. 2020. doi: 10.1371/journal.pone.0234107.

A. S. B. Karno, “Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory),†J. Inform. Inf. Secur., vol. 1, no. 1, pp. 1–8, 2020, doi: 10.31599/jiforty.v1i1.133.

D. Lien Minh, A. Sadeghi-Niaraki, H. D. Huy, K. Min, and H. Moon, “Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network,†IEEE Access, vol. 6, pp. 55392–55404, 2018, doi: 10.1109/ACCESS.2018.2868970.

J. W. G. Putra, “Pengenalan Konsep Pembelajaran Mesin dan Deep Learning Edisi 1.4 (17 Agustus 2020),†vol. 4, pp. 45–46, 2020.

J. Badriyah, A. Fariza, and T. Harsono, “Prediksi Curah Hujan Menggunakan Long Short Term Memory,†J. Media Inform. Budidarma, vol. 6, no. 3, p. 1297, 2022, doi: 10.30865/mib.v6i3.4008.

J. S. Shim, “Copyright Warning & Restrictions,†Public Health, p. 125, 2007, [Online]. Available: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Some+Contributions+on+MIMO+Radar#0

A. S. Bayangkari Karno, “Analisis Data Time Series Menggunakan LSTM (Long Short Term Memory) Dan ARIMA (Autocorrelation Integrated Moving Average) Dalam Bahasa Python.,†Ultim. InfoSys J. Ilmu Sist. Inf., vol. 11, no. 1, pp. 1–7, 2020, doi: 10.31937/si.v9i1.1223.

I. M. A. I. Agusta, A. Barakbah, and A. Fariza, “Technical Analysis Based Automatic Trading Prediction System for Stock Exchange using Support Vector Machine,†Emit. Int. J. Eng. Technol., vol. 10, no. 2, pp. 279–293, 2022, doi: 10.24003/emitter.v10i2.740.

I. H. Sarker, “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,†SN Comput. Sci., vol. 2, no. 6, pp. 1–20, 2021, doi: 10.1007/s42979-021-00815-1.

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Published

2023-07-23

Issue

Vol. 7 No. 3 (2023): Juli 2023

Section

Articles

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