Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Populer: Jurnal Penelitian Mahasiswa
Mahmudi Husain Hasbullah
Universitas Islam Sultan Agung
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Implementasi Arsitektur Temporal Fusion Transformer (Tft) Berbasis Interpretable Multi-Horizon Forecasting Prediksi Volatilitas Harga Aset Kripto Solana Mahmudi Husain Hasbullah; Ghufron Ghufron
Populer: Jurnal Penelitian Mahasiswa Vol. 5 No. 3 (2026): September : Populer: Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v5i3.5520

Abstract

The cryptocurrency market, specifically Solana (SOL), exhibits extreme volatility that is difficult for conventional algorithms to predict accurately. Additionally, advanced Deep Learning models often act as black-boxes. This study implements the Temporal Fusion Transformer (TFT) architecture for robust and interpretable multi-horizon volatility prediction of SOL/USDT. High-frequency historical data (1-hour intervals) was acquired via Binance API from January 2023 to December 2024. Data preprocessing included technical indicator feature engineering, Z-Score normalization, and chronological data splitting. Hyperparameters were optimized using Optuna. The TFT architecture performed exceptionally, achieving a Root Mean Squared Error (RMSE) of 2.5191 USDT and a Coefficient of Determination (R2) of 0.9987. Probabilistically, the model successfully mapped risk ranges with a Weighted Mean Quantile Loss of 0.4629. Explainable AI analysis revealed that predictions were heavily influenced by historical closing price, RSI, Volume, and MACD. Innovatively, TFT outputs were integrated into a Decision Support System utilizing Google Gemini 2.5-Flash to automatically generate market analysis narratives. In conclusion, this study produced a robust forecasting model, successfully resolving the black-box problem into a transparent virtual analyst assistant.
Co-Authors Ghufron Ghufron