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Lestari Handayani
Department of Informatics Engineering, UIN Sultan Syarif Kasim Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Robust cryptocurrency price forecasting using a Bayesian-optimized CNN–LSTM hybrid model Abdul Aziz Sulton; Fitri Insani; Okfalisa Okfalisa; Lestari Handayani; Nazruddin Safaat Harahap
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.377

Abstract

The rapid growth of cryptocurrency has caused the price movements of digital assets such as Bitcoin (BTC) and Ethereum (ETH) to become highly volatile and difficult to predict. This study aims to develop a cryptocurrency price prediction model using a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture optimized with Bayesian Optimization. The data used in this study consisted of daily historical data for Bitcoin and Ethereum from January 1, 2018, to December 31, 2025, obtained from Yahoo Finance. The research stages included data preprocessing, normalization using Min-Max Scaling, sequence generation using the sliding window method (window sizes of 30, 60, and 90), CNN-LSTM model development, hyperparameter optimization using Bayesian Optimization (30, 50, and 100 trials), and evaluation using regression metrics including MSE, RMSE, MAE, MAPE, and R2. The results showed that the hybrid CNN-LSTM model outperformed the standalone CNN and LSTM models, with RMSE reductions of 27% – 59% for BTC and 18% – 19% for ETH. For Bitcoin data, the best model was obtained using 30 trials with a window size of 30, achieving an RMSE of $2,588.33, MAE of $2,004.23, MAPE of 1.99%, and R2 of 0.9468. Meanwhile, for Ethereum data, the best model was obtained using 50 trials with a window size of 60, achieving an RMSE of $138.56, MAE of $99.75, MAPE of 3.27%, and R2 of 0.9737. These results indicate that the combination of CNN-LSTM and Bayesian Optimization is effective for predicting cryptocurrency prices with non-linear and volatile characteristics.
Decision support system for VARK learning style recommendation using AHP and SAW methods Mutsrin Alim; Yelfi Vitriani; Okfalisa Okfalisa; Lestari Handayani; Muhammad Affandes
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.403

Abstract

To establish an objective mechanism for recommending tailored educational approaches, this research focuses on the architectural design of a DSS grounded in the VARK framework, which classifies preferences into visual, auditory, textual, and physical modalities. The operational framework combines AHP and SAW to eliminate subjectivity from the evaluation process. While the extraction of criteria importance factors relied on AHP, utilizing qualitative insights from a psychometrics expert in the field of psychology, the subsequent prioritization of pedagogical options was executed via SAW. Four core dimensions formed the basis of this evaluation, specifically focusing on how individuals perceive stimuli, process information, select instructional materials, and adapt to environmental settings. The initial matrix derivation yielded uniform importance coefficients of 0.25 across all dimensions, supported by a CR of 0 to verify the logical coherence of the expert input. Structurally, the platform was deployed as a responsive web system powered by the Laravel architecture and backed by a MySQL database engine. To confirm computational integrity, the algorithmic outputs generated by the software were audited against traditional manual calculations, resulting in a perfect mathematical alignment. Consequently, the empirical evidence confirms that the engineered DSS offers a precise diagnostic tool, thereby enabling learners to discover optimal educational pathways that correspond directly with their psychological profiles.
Co-Authors Abdul Aziz Sulton Fitri Insani Muhammad Affandes Mutsrin Alim Nazruddin Safaat Harahap Okfalisa Okfalisa Yelfi Vitriani