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Alfarizi, Muhammad
Bina Nusantara University
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Predicting Cryptocurrency Prices Using Machine Learning: A Case Study on Bitcoin Alfarizi, Muhammad; Lestarini, Dinda
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11234

Abstract

The rapid growth of cryptocurrencies, particularly Bitcoin, has drawn significant attention from investors and researchers due to its extreme price volatility. However, predicting the price of Bitcoin against the Indonesian Rupiah (BTC/IDR) remains a major challenge, especially in emerging markets such as Indonesia. This study aims to conduct an empirical comparison among three deep learning models Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (CNN-1D) in forecasting Bitcoin prices based on historical data obtained from the Indodax platform for the period 2018–2025. The dataset consists of five main variables: opening price, highest price, lowest price, closing price, and trading volume. Prior to model training, preprocessing steps were conducted, including handling missing values using the forward fill method, normalization with MinMaxScaler, and constructing time series data with a 60-day look-back window. The models were trained using an 80% training and 20% testing data split, the Adam optimizer, Mean Squared Error (MSE) as the loss function, for 50 epochs with a batch size of 32. Evaluation was performed using five quantitative metrics: MSE, RMSE, MAE, MAPE, and R², along with validation techniques to prevent data leakage. The results indicate that the GRU model achieved the best performance, with a MAPE of 1.77% and an R² of 0.9916, outperforming LSTM (MAPE 3.90%) and CNN-1D (MAPE 6.17%). These findings suggest that GRU is computationally more efficient and better adapted to nonlinear temporal dependencies in highly volatile markets. This research contributes to the academic discourse on the application of deep learning for digital asset price forecasting and provides practical implications for investors and developers of financial predictive systems in Indonesia. Future studies are expected to explore hybrid models or multi-step forecasting approaches to enhance real-time predictive performance.
Co-Authors Abdul Azis Abdul Halim Hasugian Al Hakim, Muhammad Rafif Al-Faqih, Ali Sofyan Andriana Firmansyah, Almayda Anggraeni, Deby Gita Apriani, Yosi Arifian, Rafialdo Arofah, Ulfa Arwandi, Jhon Berliana Br Bangun, Eka Uliyanti Putri DEWI SARTIKA Dinda Lestarini Dodik Hartono Edmizal, Eval Fairus, Aisyah Nur Farhan, Muhammad Raja Farizal Saputra Gita Anggraeni, Deby H, Syaibatul Aslamiyah Hakim, Muhammad Saiful Hanum, Rastinia Kamila Hazizah, Siti Nur Hendrawan, Jodi Ikhsan, Ridho Bramulya Ira Rahmawati Irika Widiasanti Jane, Antonia Felicia Julpiani, Julpiani Kaltsum, Salma Nabilla Kamila Hanum, Rastinia Kartanegara, Rd. Mulyadhi Khamdalah, Retno Makhtufir Kirana, Sandi Kurnia Sari, Rini Lantin Sulistiyorini, Lantin M.F., Muhammad Fathan Marfuah Marfuah Mariani Mariani Maulida, Ahmad Fikron Megawati, Suci Muhammad Prawibowo Mulyadi, Sandi Hidayutul Nabilah Khairunnisa Darwin Nafolion Nur Rahmat, Nafolion Nur Nelwida Nelwida Ngatindriatun Ngatindriatun Ngatindriatun Ngatindriatun Noer, Lissa Rosdiana Novitasari, Ida Nugroho, Ramadhan Aryo Nurhayati Oktafiani, Astrid Wardani Oktaviani, Wiwin A Panita, Shakila Peni Perdani Juliningrum, Peni Perdani Perdana, Akmal Perdana, Ayu Lestari Primirti, Irmarawati Dia Putri Tunggal Putriani, Dian Qanita, Aisyah Rafialdo Arifian Rafialdo Arifian Rahmawati, Iis Ramadhan, Noval Anki Rini Kurnia Sari Rini Kurnia Sari Rini Kurnia Sari Rini Kurnia Sari, Rini Kurnia Rizka Yunita Rosdiana Noer, Lissa saleh, zulkifli Saputra, Baruna Saputri, Nelsa Satrio, Rizky Wahyu Septiani, Indri Setyapratiwi, Hela Sri Wahyuni Suhari Suhari Taronisokhi Zebua Tatang Mulyana, Salma Nabilla Kaltsum Tehuayo, Haris Thahur, Dhiya Tjung Hauw Sin, Tjung Hauw Tumanggor, Rahmayanti Uma Khumairoh Wijaya, Krisna Nata Wijaya, Lianna Wiwin Addriani Mangngampe Wusqo, Urwatul Yulia Rachmawati Yuniarty Yuniarty Zalika, Zela Zela Zalika