cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andhika Rafi Hananto
Contact Email
andhikarh90@gmail.com
Phone
+6282314736799
Journal Mail Official
support@jdmdc.com
Editorial Address
Graha Permata Estate, Jl. HM Bahrun Blok H9, Sokayasa, Berkoh, Kec. Purwokerto Tim., Kabupaten Banyumas, Jawa Tengah 53146
Location
Kab. banyumas,
Jawa tengah
INDONESIA
Journal of Digital Market and Digital Currency
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30480981     DOI : https://doi.org/10.47738/jdmdc
Core Subject : Economy, Science,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
Journal of Digital Market and Digital Currency publishes high-quality research on: Digital Marketing Digital Currencies Cryptocurrency Trends Blockchain Applications Fintech Innovations Our goal is to provide a platform for researchers, practitioners, and policymakers to share innovative findings, discuss emerging trends, and address the challenges and opportunities presented by the Journal of Digital Market and Digital Currency.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 45 Documents
 1   2   3   4   5 
Machine Learning-Based Forecasting of AAVE Cryptocurrency: A Comparative Study of Regression, Ensemble, and Deep Learning Models Monika Łobaziewicz
Journal of Digital Market and Digital Currency Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i2.62

Abstract

The volatility of cryptocurrency markets has increased the demand for accurate forecasting models that can help investors and analysts anticipate price movements. This study evaluates the predictive performance of four machine learning algorithms, namely Linear Regression, Random Forest, XGBoost, and Long Short-Term Memory (LSTM), in forecasting the closing price of the AAVE cryptocurrency. The models were trained using historical market data consisting of key indicators such as Open, High, Low, Volume, and Marketcap. Their performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R²). The results show that Linear Regression produced the most accurate predictions with the lowest MAE (8.13), RMSE (8.76), and the highest R² (0.9924). Random Forest and XGBoost also achieved good results with R² values of 0.9337 and 0.9484, respectively, while the LSTM model performed poorly with an R² of 0.4328. The study concludes that simpler models can outperform more complex algorithms when the dataset is limited and exhibits linear behavior. The findings emphasize that model selection in cryptocurrency forecasting should consider data structure and quantity. Future work should involve larger datasets, higher-frequency data, and hybrid models that integrate ensemble learning and deep learning for improved predictive accuracy.
A Hybrid SARIMAX–LSTM Framework for Predicting Price Volatility in High-Tech Digital Markets: Evidence from NVIDIA Flurey Martin
Journal of Digital Market and Digital Currency Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i2.63

Abstract

This study develops a Hybrid SARIMAX–LSTM model to improve the accuracy and robustness of stock price forecasting in digital and volatile financial markets. The model combines the linear and seasonal forecasting strengths of the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) with the nonlinear learning capability of the Long Short-Term Memory (LSTM) network. Using historical data from NVDIA Corporation, the hybrid framework was optimized through smart weighting to balance the contribution of both components. The results show that the model achieved a Root Mean Square Error (RMSE) of 8.59 and a coefficient of determination (R²) of 0.9166, indicating that over 91 percent of price variance was accurately explained. Residual analysis confirmed unbiased predictions with normally distributed errors, demonstrating high stability and adaptability under volatile market conditions. Compared with individual models, the hybrid approach produced smoother and more consistent forecasts. Overall, the Hybrid SARIMAX–LSTM framework offers an interpretable and reliable tool for digital market forecasting and AI-based financial decision-making.
A Reinforcement Learning Approach to Bitcoin Trading: Proximal Policy Optimization with Trend-Following and Risk-Aware Reward Design Victor Vladareanu
Journal of Digital Market and Digital Currency Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i2.64

Abstract

This study proposes a reinforcement learning based trading strategy for Bitcoin using Proximal Policy Optimization with a trend following and risk aware reward design. The model is developed within a custom trading environment that incorporates multiple technical indicators, including trend, momentum, and volatility features, to capture market dynamics. A continuous action space is employed to enable flexible portfolio allocation between cash and Bitcoin, allowing the agent to learn dynamic position sizing rather than discrete buy or sell decisions. The reward function is designed to encourage profit generation while penalizing excessive risk, trading activity, and drawdowns. The proposed model is evaluated on historical Bitcoin data and compared with a Buy and Hold baseline using metrics such as total return, Sharpe ratio, maximum drawdown, trading frequency, and transaction costs. The results show that while the PPO strategy does not outperform Buy and Hold in terms of total return, it achieves superior risk adjusted performance with a higher Sharpe ratio and more stable portfolio growth. However, the model exhibits high trading frequency, leading to increased transaction costs that reduce overall profitability. These findings demonstrate that reinforcement learning offers a promising approach for developing adaptive and risk sensitive trading strategies, although further improvements are required to enhance trading efficiency and cost management.
Enhancing Loan Approval Prediction Using Ensemble Machine Learning Techniques Through Comprehensive Model Comparison and Performance Evaluation Analysis Qing Tan
Journal of Digital Market and Digital Currency Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i2.65

Abstract

Loan approval prediction is a crucial task in the financial sector, as it directly impacts risk management and decision-making processes. This study aims to enhance the accuracy of loan approval prediction by applying ensemble machine learning techniques and comparing their performance with a baseline model. The dataset used in this study contains borrower demographic, financial, and employment-related attributes, and missing values were handled using a deletion method to ensure data consistency. Several models were implemented, including Logistic Regression as the baseline model, as well as ensemble methods such as Random Forest, Gradient Boosting, and Voting Classifier. The models were evaluated using multiple performance metrics, including Accuracy, Precision, Recall, F1-Score, and ROC-AUC. The experimental results show that ensemble models consistently outperform the baseline model across all evaluation metrics. Random Forest achieved the highest ROC-AUC, indicating superior discriminative capability, while the Voting Classifier provided the best balance between precision and recall, resulting in the highest F1-Score. In addition, feature importance analysis revealed that CreditScore, Income, and Employment Type are the most influential factors in loan approval decisions. These findings demonstrate that ensemble learning methods are effective in improving predictive performance and can provide reliable support for loan approval decision-making systems.
Machine Learning-Based Fraud Detection in Banking Transactions Using Integrated Behavioral and Transactional Feature Engineering with Weak Labeling Approach Hongbo Wang
Journal of Digital Market and Digital Currency Vol. 3 No. 2 (2026): Regular Issue June 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i2.66

Abstract

Fraud detection in banking transactions is a critical challenge due to the imbalanced nature of data and the lack of labeled fraud instances. This study proposes a machine learning approach for detecting fraudulent transactions by integrating behavioral and transactional features, combined with a rule-based weak labeling strategy to generate fraud labels. The dataset consists of 2,512 banking transactions, with 14.29% labeled as fraud. Three models were evaluated, including Logistic Regression, Random Forest, and XGBoost, using stratified cross-validation and multiple evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The results show that ensemble-based models outperform Logistic Regression, with Random Forest achieving the best balance between precision and recall, and XGBoost obtaining perfect recall and the highest ROC-AUC, indicating its strong ability to detect fraudulent transactions. Feature importance analysis reveals that transaction amount and deviation from typical user behavior are key indicators of fraud. Despite these promising results, the study is limited by the use of rule-based labeling and a relatively small dataset. Future work should focus on validating the proposed approach using real-world labeled data and improving model robustness for practical deployment.

Page 5 of 5 | Total Record : 45

 1   2   3   4   5 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2026): Regular Issue June 2026 Vol. 3 No. 1 (2026): Regular Issue March 2026 Vol. 2 No. 4 (2025): Regular Issue December 2025 Vol. 2 No. 3 (2025): Regular Issue September 2025 Vol. 2 No. 2 (2025): Regular Issue June 2025 Vol. 2 No. 1 (2025): Regular Issue March Vol. 1 No. 1 (2024): Regular Issue June 2024 Vol. 1 No. 3 (2024): Regular Issue December Vol. 1 No. 2 (2024): Regular Issue September