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All Journal Jambura Journal of Mathematics Journal of Novel Engineering Science and Technology
Pasha, Muhammad Fermi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science Mathematics Mechanical Engineering

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Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham Simamora, Fandi Presly; Purba, Ronsen; Pasha, Muhammad Fermi
Jambura Journal of Mathematics Vol 7, No 1: February 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i1.27166

Abstract

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.
Optimization of hybrid-based Collaborative Filtering using Matrix Factorization, Feedforward Neural Network, and XGBoost Gulo, Filimantaptius; Purba, Ronsen; Pasha, Muhammad Fermi
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): In Press - Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.1356

Abstract

Collaborative filtering recommendation systems are widely used in digital applications; however, they still face challenges such as cold-start and first-rater problems, as well as limited accuracy due to their inability to capture complex user–item relationships. This study proposes a hybrid recommendation model that integrates Matrix Factorization, MLP-based Feedforward Neural Network (MLP) and Extreme Gradient Boosting (XGBoost). Experiments were conducted on two real-world datasets, namely MovieLens (movies) and PT XYZ (hotels), to validate the effectiveness of the proposed approach. The results indicate that the hybrid model consistently outperforms baseline methods such as SGD-based Matrix factorization, Matrix factorization +MLP, and user/item-based Collaborative filtering. Specifically, the integration of nonlinear learning through MLP and feature enhancement via XGBoost significantly improves prediction accuracy while mitigating cold-start and first-rater issues. These findings suggest that hybrid machine learning–based approaches can advance the development of more adaptive, accurate, and personalized recommendation systems.
Co-Authors Fandi Presly Simamora Gulo, Filimantaptius Ronsen Purba