Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Yogi Isro' Mukti
Unknown Affiliation
Computer Science & IT

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

Found 1 Documents
Search

 1 
Prediksi Cryptocurrency Berbasis LSTM Menggunakan Multi Modal Indikator Trading(Studi: Ethereum dan Solana) Arya; Yogi Isro' Mukti; Alfis Arif; -, Sigit Candra Setya
BETRIK Vol. 17 No. 01 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/83kqq228

Abstract

The dynamic development of the cryptocurrency market causes digital asset prices to experience high volatility, making it difficult for investors to accurately predict price movements. Therefore, an analytical method is needed to model price movement patterns in time series data. This study aims to develop a cryptocurrency price prediction model for Ethereum and Solana using the Long Short-Term Memory (LSTM) method with a multi-modal trading indicator approach. The dataset used consists of historical price data including open, high, low, close, trading volume, and technical indicators such as Exponential Moving Average (EMA), Relative Strength Index (RSI), and Bollinger Bands. The research process follows the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modelling, evaluation, and deployment stages. The data were processed through normalization and time series windowing, with a training and testing data split of 80:20. The evaluation results using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) indicate that the model has good predictive performance. The Ethereum model produced an RMSE value of 129.08 and a MAPE of 3.26%, while the Solana model produced an RMSE of 8.30 and a MAPE of 3.63%. The developed model was also implemented in a Streamlit-based dashboard to visualize prediction results interactively, helping users monitor and analyze cryptocurrency price movements.
Co-Authors -, Sigit Candra Setya Alfis Arif Arya