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Journal : Jurnal Algoritma

Implementation of Long Short-Term Memory Algorithms on Cryptocurrency Price Prediction with High Accuracy on Volatile Assets Nursiana Zasqia, Andi Nirina; Laila, Rahmah; Trezandy Lapatta, Nouval; Yazdi Pusadan, Mohammad; Santi, Dessy; Wirdayanti
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2422

Abstract

Cryptocurrencies have emerged as one of the most popular digital assets, characterized by high volatility, which presents a significant challenge in forecasting their price movements accurately. This study aims to implement the Long Short-Term Memory (LSTM) algorithm to predict the prices of selected cryptocurrencies, including Bitcoin (BTC), Binance Coin (BNB), Ethereum (ETH), Dogecoin (DOGE), Solana (SOL), and Shiba Inu (SHIB). The LSTM model is trained using the Adam optimizer and employs early stopping to mitigate overfitting. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results indicate that the LSTM model achieves strong predictive accuracy for relatively low-volatility assets such as Dogecoin and Solana, with R² scores of 0.9795 and 0.9523, respectively. In contrast, its performance declines when applied to highly volatile assets like Bitcoin and Binance Coin. The findings also suggest that LSTM performs best in short-to-medium-term forecasts (7 to 30 days), but shows limitations in long-term predictions. This study contributes to the field by demonstrating the applicability of LSTM in financial forecasting and highlighting its strengths and constraints across different volatility profiles. Practically, the findings can assist traders and financial analysts in making data-driven decisions by applying LSTM models for more reliable short-term predictions, while emphasizing the need to integrate external market factors to enhance long-term forecast accuracy.
Co-Authors A Wahab Jufri Absor, Sholihul Afiah, Nurul Anggun Ajizah, Nanda Syaharotulo Al Hakim, Roby Maulana Alviah, Azza Anggreni, Dwi Shinta Anita Ahmad Kasim Anita, Ayu Anwar, Asriani Azkia, St. Aulia Billyardi Ramdhan Bogheiry, Ali Chirzun, Ahmad Damayanti, Sherli Darojah, Murtafiatun Deny Wiria Nugraha Dessy Santi Diana Wahyuni Sulasti Dwi Shinta Angreni Dwiyanto, Andika Fahrudda, Ansarul Fajriyah, Nurul Faldiansyah, Faldiansyah Firhan Nurfaizi Hafid, Rosandi Hajra Rasmita Ngemba Haniifah, Ulaa Hartamto, Offia Melda Permata Imam Abdullah , Ahmad Imam Wahyudi Indrawan, Imam Wahyudi Imas Hasdianti Jamil, Ahmad Mochtar Jonathan Wongkar, Noel Marcell Joned Bangkit Wahyu Laksono Jujun Ratnasari Kasaedja, Tafania Natalia Laala, Jonathan Zebina Lamadjido, Moh. Raihan Dirga Putra Lamasitudju, Chairunnisa M Thaha, M M Zakaria Meyssa Dwi Miftah Miftah Miftah, Miftah Mohammad Yazdi Pusadan Muhammad Anas Muhammad Jindan Mundakir Nafiah, Salwa Ngitung, Putri Aisyah Ningsih, Alief Surya Nouval Trezandy Lapatta Nugroho, Yudhistiro Andri Nurholisoh, Siti Nursalim, Moh. Agung Nursiana Zasqia, Andi Nirina Pratama, Moh. Asry Eka Pratama, Moh. Rezki Pratiwi, Dian Asri Purnomo, Syalsabila Aulia Puspita, Eka Ari Qothrunnada, Widya Rahmasena, Naomi Rasmita Ngemba, Hajra Rifai, Muhammad Fajar Rima Ahadiah Rini Septiani Rinianty, Rinianty Risaldi Pata’Dungan, Adi Rita Arlitia Rizka Ardiansyah Rosmala Nur Ruddy Indra Frahasta Rumampuk, Viola Gracella Ryfial Azhar Saleh, Muhammad Taufik Salhudin, Salhudin Satrio, Giri Selpi Susilawati Septiano Anggun Pratama Setiono Setiono Sibawaihi, Muhammad Silvi Husnaini Siti Jamilah Siti Khodijah Parinduri Sri Purnawati SRI RAHAYU Sudharsono, Muhamad Suswojo, Heru Syahrullah Syahrullah Syahrullah Syaiful Hendra Teti Damayanti Thaha, M. Thaha TITIN SUNARYATI Ulfa Fauzi Vira Safitri Aulia Widyati, Made Ayu Sri Wilda Waliam Wirdayanti Wiska Hera Yuri Yudhaswana Joefrie Yusuf Anshori