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JUTIM (Jurnal Teknik Informatika Musirawas)
Published by Universitas Bina Insan Lubuklinggau
ISSN : 25411888     EISSN : 26145782     DOI : https://doi.org/10.32767/jutim
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Other
Jurnal Teknik Informatika Musirawas merupakan Jurnal ilmiah yang meliputi kompetensi Human Computer Interaction, IT Governance, Networking Technology, New Media Technology, Information Search Engine, Multimedia, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Computer Network Security, Natural Language Processing, Software Engineering, Programming Methodology and Paradigm, Data Engineering, Information Management, Knowledge Based Management System, Game Technology Jurnal Teknik Informatika Musirawas terbit sebanyak dua kali dalam satu tahun, pada bulan Juni dan Desember.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 241 Documents
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ANALISIS FAKTOR YANG MEMPENGARUHI PREDIKSI HARGA BITCOIN DAN ETHEREUM MENGGUNAKAN EXPLAINABLE AI (SHAP) DAN ALGORITMA MACHINE LEARNING Agusto, Kevin Grado; Maulany, Raymond
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 11 No 1 (2026): JUTIM (Jurnal Teknik Informatika Musirawas) Maret
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v11i1.2955

Abstract

The high volatility of cryptocurrency assets such as Bitcoin and Ethereum poses significant challenges for producing accurate and transparent price predictions. This instability is driven by factors including trading volume, market sentiment, and global economic conditions, requiring predictive models that not only forecast price movements but also explain the contribution of each variable. This study analyzes the factors influencing Bitcoin and Ethereum price prediction using a Long Short-Term Memory (LSTM) model integrated with Explainable Artificial Intelligence (XAI) through SHAP (SHapley Additive exPlanations). The dataset was obtained from Yahoo Finance covering January 2022–December 2024. Model evaluation using MAE, MSE, RMSE, and the coefficient of determination (R²) indicates that the model captures the main price movement trends, although it shows limitations in representing extreme short-term fluctuations due to the high volatility of cryptocurrency markets. Autocorrelation Function (ACF) analysis of residuals suggests that the primary temporal patterns have been effectively learned by the model. SHAP analysis identifies Low, High, and short-term Moving Average (MA7) as the most influential features. Overall, the integration of LSTM and XAI enables interpretable cryptocurrency price prediction, supporting a more transparent and data-driven understanding of market dynamics and investment decision-making.

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