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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| Format: | Article |
| Language: | English |
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Department of Mathematics, Universitas Negeri Gorontalo
2025-02-01
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| Series: | Jambura Journal of Mathematics |
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| Online Access: | https://ejurnal.ung.ac.id/index.php/jjom/article/view/27166 |
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| author | Fandi Presly Simamora Ronsen Purba Muhammad Fermi Pasha |
| author_facet | Fandi Presly Simamora Ronsen Purba Muhammad Fermi Pasha |
| author_sort | Fandi Presly Simamora |
| collection | DOAJ |
| description | 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 hyperparameter configurations. This research aims to find the optimal hyperparameter configuration for the BiLSTM model using Bayesian Optimization. The study was conducted using three blue-chip stocks from different sectors, namely BBCA, BYAN, and TLKM, with two scenarios of search iterations. The test results show that Bayesian Optimization was able to find the optimal hyperparameter configuration for the BiLSTM model, with the best MAPE values for each stock: BBCA 1.2092%, BYAN 2.0609%, and TLKM 1.2027%. Compared to previous research on Grid Search-BiLSTM, the use of Bayesian Optimization-BiLSTM resulted in lower MAPE values. |
| format | Article |
| id | doaj-art-73de637be9c44475978c25f6ce2ca005 |
| institution | DOAJ |
| issn | 2654-5616 2656-1344 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Department of Mathematics, Universitas Negeri Gorontalo |
| record_format | Article |
| series | Jambura Journal of Mathematics |
| spelling | doaj-art-73de637be9c44475978c25f6ce2ca0052025-08-20T02:54:54ZengDepartment of Mathematics, Universitas Negeri GorontaloJambura Journal of Mathematics2654-56162656-13442025-02-017181310.37905/jjom.v7i1.271668881Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga SahamFandi Presly Simamora0Ronsen Purba1Muhammad Fermi Pasha2Universitas MikroskilUniversitas MikroskilUniversitas MikroskilThe 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 hyperparameter configurations. This research aims to find the optimal hyperparameter configuration for the BiLSTM model using Bayesian Optimization. The study was conducted using three blue-chip stocks from different sectors, namely BBCA, BYAN, and TLKM, with two scenarios of search iterations. The test results show that Bayesian Optimization was able to find the optimal hyperparameter configuration for the BiLSTM model, with the best MAPE values for each stock: BBCA 1.2092%, BYAN 2.0609%, and TLKM 1.2027%. Compared to previous research on Grid Search-BiLSTM, the use of Bayesian Optimization-BiLSTM resulted in lower MAPE values.https://ejurnal.ung.ac.id/index.php/jjom/article/view/27166bilstmbayesian optimizationhyperparameter tuningstock price prediction |
| spellingShingle | Fandi Presly Simamora Ronsen Purba Muhammad Fermi Pasha Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham Jambura Journal of Mathematics bilstm bayesian optimization hyperparameter tuning stock price prediction |
| title | Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham |
| title_full | Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham |
| title_fullStr | Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham |
| title_full_unstemmed | Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham |
| title_short | Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham |
| title_sort | optimisasi hyperparameter bilstm menggunakan bayesian optimization untuk prediksi harga saham |
| topic | bilstm bayesian optimization hyperparameter tuning stock price prediction |
| url | https://ejurnal.ung.ac.id/index.php/jjom/article/view/27166 |
| work_keys_str_mv | AT fandipreslysimamora optimisasihyperparameterbilstmmenggunakanbayesianoptimizationuntukprediksihargasaham AT ronsenpurba optimisasihyperparameterbilstmmenggunakanbayesianoptimizationuntukprediksihargasaham AT muhammadfermipasha optimisasihyperparameterbilstmmenggunakanbayesianoptimizationuntukprediksihargasaham |