PREDIKSI HARGA SAHAM MENGGUNAKAN ALGORITMA NEURAL NETWORK
DOI:
https://doi.org/10.47111/jti.v18i1.11303Abstract
In recent years, the development of technology and artificial intelligence has brought forth new opportunities in analyzing and predicting stock prices. One of the approaches used is the Neural Network algorithm, which is a part of the branch of artificial intelligence known as Deep Learning. This algorithm can learn complex patterns and relationships among data by modeling inspired by the human neural network. This research utilizes the Neural Network for stock price prediction and aims to understand the application of Neural Network in predicting stock prices, which can benefit investors and market participants. Additionally, historical stock price data can be used as input for the Neural Network algorithm. The Neural Network is a frequently used algorithm for accurate predictions and is widely employed in prediction-based or forecasting research. The result of this research is the Root Mean Squared Error (RMSE) value of 19.734 +/- 0.000. The use of the Neural Network as an algorithm for stock price prediction provides investors with valuable information for making investment decisions for companies..
Downloads
References
X. Pang, Y. Zhou, P. Wang, W. Lin, and V. Chang, “An innovative neural network approach for stock market prediction,” Journal of Supercomputing, vol. 76, no. 3, pp. 2098–2118, Mar. 2020, doi: 10.1007/s11227-017-2228-y.
Y. Umaidah, “PENERAPAN ALGORITMA ARTIFICIAL NEURAL NETWORK DALAM PREDIKSI HARGA SAHAM LQ45 PT. BANK RAKYAT INDONESIA, TBK,” 2018.
P. Bs1, M. Shastry, and M.- Juni, “Prediksi Harga Saham Menggunakan LSTM”.
M. Z. Jiaa, J. Huangb, L. Pangcdan, and Q. Zhaod, “Analisis dan Penelitian Harga Saham LSTM dan Jaringan Syaraf LSTM Dua Arah,” 2019. [Online]. Available: www.onlinedoctranslator.com
Y. Hao and Q. Gao, “Predicting the trend of stock market index using the hybrid neural network based on multiple time scale feature learning,” Applied Sciences (Switzerland), vol. 10, no. 11, Jun. 2020, doi: 10.3390/app10113961.
P. Gao, R. Zhang, and X. Yang, “The Application of Stock Index Price Prediction with Neural Network,” Mathematical and Computational Applications, vol. 25, no. 3, p. 53, Aug. 2020, doi: 10.3390/mca25030053.
F. Kamalov, “Forecasting significant stock price changes using neural networks,” Nov. 2019, doi: 10.1007/s00521-020-04942-3.
N. K. E. Sapitri, I. P. E. N. Kencana, and L. P. I. Harini, “Penerapan Artificial Neural Network (ANN) untuk Memprediksi Perubahan Derajat Miopia pada Manusia,” Jurnal Matematika, vol. 10, no. 1, p. 53, Oct. 2020, doi: 10.24843/jmat.2020.v10.i01.p123.
C. Anand, “Comparison of Stock Price Prediction Models using Pre-trained Neural Networks,” Journal of Ubiquitous Computing and Communication Technologies, vol. 3, no. 2, pp. 122–134, Jul. 2021, doi: 10.36548/jucct.2021.2.005.
V. R. Prasetyo, “Anasthasya Averina 3) , Lauren Sunyoto 4) , Budiarjo 5) 1), 2), 3), 4), 5) Jurusan Teknik Informatika, Fakultas Teknik,” 2022.
Ananda Riska Mita Izati, “THE APPLICATION OF ARTIFICIAL NEURAL NETWORK METHOD IN FORECASTING THE NUMBER OF PREGNANT WOMAN VISITS (K4)”.
D. Selvamuthu, V. Kumar, and A. Mishra, “Indian stock market prediction using artificial neural networks on tick data,” Financial Innovation, vol. 5, no. 1, Dec. 2019, doi: 10.1186/s40854-019-0131-7.
