Prediksi Harga Saham Twitter Dengan Long Short-Term Memory Recurrent Neural Network

  • Ibnu Akil (1*) Universitas Bina Sarana Informatika
  • Indra Chaidir (2)

  • (*) Corresponding Author
Keywords: stock prices prediction, recurrent neural network, long short-term memory

Abstract

Abstract— Today the trading business has become a trend to get money easily without having to work hard as long as you have capital. To get maximum results and avoid losses, it is necessary to have expertise in predicting the ups and downs of the stock market value. The purpose of this research is to utilize machine learning technology to predict the fluctuation of stock value by using the Long Short-Term Memory RNN model. From the results of this study, it was found that LSTM+RNN is suitable for use in single-step models.

Keywords: stock price, machine learning, recurrent neural network, lstm

Abstrak—Dewasa ini bisnis trading menjadi suatu trend untuk mendapatkan uang dengan mudah tanpa harus bekerja keras asalkan memiliki modal. Untuk mendapatkah hasil yang maksimal dan menghindari kerugian maka diperlukan keahlian di dalam memprediksi naik turunya nilai bursa saham. Tujuan dari penelitian ini adalah memanfaatkan teknologi machine learning untuk memprediksi naik turunya nilai saham dengan menggunakan model Long Short-Term Memory RNN. Dari hasil penelitian ini didapatkan bahwa LSTM+RNN cocok untuk digunakan pada model single-step.

Kata kunci: harga saham, machine learning, recurrent neural network, lstm

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References

Afifah, L. (2022, Juli 12). 3 Metode Normalisasi Data (Feature Scaling) di Python. Retrieved from ilmudatapy.com: https://ilmudatapy.com/metode-normalisasi-data/

Chamidah, N., Wiharto, & Salamah, U. (2012). Pengaruh Normalisasi Data pada Jaringan Syaraf Tiruan Backpropagation Gradient Descent (BPGDAG) untuk Klasifikasi. Jurnal ITSmart.

Chen, L.-P. (2020). Using Machine Learning Algorithms on Prediction of Stock Price. Journal of Modeling and Optimization, 84-99.

Hochreiter, S., & Schmidhubber, J. (1997). Long Short-Term Memory. Neural Computation.

Janeski, M., & Kalajdziski, S. (2010). Forecasting Stock Market Prices. ICT Innovations 2010.

Mandala, P. W., Wahyuni, M. A., & Atmadja, A. T. (2019). Determinasi Trader Dalam Pengambilan Keputusan Analisis Trading di Pasar Valas. JIMAT, 161-172.

Ong, E. (2008). Technical Analysis for Mega Profit. Jakarta: Gramedia.

Redaksi OCBC. (2022, 04 26). Trading Saham: Definisi, Cara, dan Bedanya dengan Investasi. Retrieved from OCBC NISP Web site: https://www.ocbcnisp.com/id/article/2021/11/04/trading-saham

Titin. (2015). Analisis Pengambilan Keputusan dalam Transaksi Trading Forex di Fxindo Regional Lamongan. Jurnal Ekbis, 689-695.

Yi, Z., & Tan, K. (2004). Convergence Analysis of Recurrent Neural Network. Springer Science + Business media.

Yuliara, I. M. (2016, Maret). Regresi Linear Sederhana. Regresi Linear Sederhana. Universitas Udayana.

Zen, H., & Sak, H. (2015). Unidirectional Long Short-Term Memory Recurrent Neural Network With Recurrent Output Layer for Low-Latency Speech Sythesis. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
Published
2022-08-10
How to Cite
Akil, I., & Chaidir, I. (2022). Prediksi Harga Saham Twitter Dengan Long Short-Term Memory Recurrent Neural Network. INTI Nusa Mandiri, 17(1), 1 - 7. https://doi.org/10.33480/inti.v17i1.3277
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