Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham

The accuracy of deep learning models in predicting dynamic and non-linear stock market data highly depends on selecting optimal hyperparameters. However, finding optimal hyperparameters can be costly in terms of the model's objective function, as it requires testing all possible combinations of...

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Bibliographic Details

Published in:	Jambura Journal of Mathematics
Main Authors:	Fandi Presly Simamora, Ronsen Purba, Muhammad Fermi Pasha
Format:	Article
Language:	English
Published:	Department of Mathematics, Universitas Negeri Gorontalo 2025-02-01
Subjects:	bilstm bayesian optimization hyperparameter tuning stock price prediction
Online Access:	https://ejurnal.ung.ac.id/index.php/jjom/article/view/27166