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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 5 Documents
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Search results for , issue "Vol. 2 No. 4 (2025): Regular Issue December 2025" : 5 Documents clear
Enhancing Short-Term Price Prediction of TON-IRT Using LSTM Neural Networks: A Machine Learning Approach in Blockchain Trading Analytics Stephanus, Alphin; Mbitu, Elisabeth Tansiana
Journal of Digital Market and Digital Currency Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i4.43

Abstract

This study explores the application of Long Short-Term Memory (LSTM) neural networks for predicting short-term price movements of the TON-IRT trading pair in the cryptocurrency market. Given the high volatility and complexity of cryptocurrency prices, traditional models like Linear Regression and ARIMA often fail to capture the underlying non-linear and temporal dependencies. To address this, we implemented an LSTM model, a type of recurrent neural network specifically designed for sequential data. The model was trained on historical hourly data, utilizing various technical indicators and lagged features to improve prediction accuracy. Our results demonstrated that the LSTM model significantly outperformed traditional methods, achieving a Mean Absolute Error (MAE) of 0.0274, a Root Mean Squared Error (RMSE) of 0.0321, and an R-squared (R²) value of 0.8743, which indicated that the model captured over 87% of the variance in the actual price data. Visual analysis of predicted versus actual prices revealed a strong alignment, though some lag in predictions during high-volatility periods was observed. The model also showed a tendency to underestimate price peaks, highlighting areas for further refinement. This study contributes to the field of blockchain trading analytics by demonstrating the effectiveness of LSTM models in addressing the unique challenges of cryptocurrency price prediction. Practical implications for traders and investors include the ability to enhance trading strategies, optimize entry and exit points, and improve risk management. Future research could integrate additional external factors, such as market sentiment and news events, or explore advanced architectures like Transformer models. By doing so, the predictive capabilities of LSTM models in volatile markets like cryptocurrency could be further refined, leading to more robust and accurate forecasting tools for financial decision-making.
Volatility and Risk Assessment of Blockchain Cryptocurrencies Using GARCH Modeling: An Analytical Study on Dogecoin, Polygon, and Solana Alkhoze, Mona; Almasre, Miada
Journal of Digital Market and Digital Currency Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i4.44

Abstract

This study analyzed the volatility and risk profiles of three prominent blockchain-based cryptocurrencies—Dogecoin, Polygon, and Solana—using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. Volatility, a key risk metric for cryptocurrencies, was modeled through the GARCH(1,1) framework, which effectively captured the time-varying nature of price fluctuations. The analysis revealed that Dogecoin exhibited the highest volatility and risk, primarily driven by its speculative market behavior and social media influence. Polygon and Solana, while also volatile, demonstrated more stability, with their risk profiles reflecting the technological advancements and broader use cases within their respective blockchain ecosystems. The study also incorporated Value at Risk (VaR) and Conditional Value at Risk (CVaR) metrics to assess the potential downside risks for each cryptocurrency. Dogecoin had the highest potential for extreme losses, followed by Polygon and Solana. The GARCH model successfully identified the volatility persistence in these assets, showing that past market conditions heavily influenced future volatility. This research contributes to the literature on cryptocurrency volatility by applying the GARCH(1,1) model to analyze digital assets with varying market characteristics. The findings emphasize the need for robust risk management strategies tailored to the unique behaviors of individual cryptocurrencies. Limitations of the study included the use of historical data and the focus on only three cryptocurrencies, suggesting opportunities for future research. Potential areas for further study include the incorporation of additional variables, such as macroeconomic indicators, and the exploration of alternative volatility models, such as EGARCH or TGARCH, to better capture the complexities of cryptocurrency markets. These insights provide valuable guidance for investors, risk managers, and policymakers navigating the volatile and evolving landscape of blockchain-based digital assets.
Understanding User Satisfaction in Digital Finance Through Sentiment Analysis of User Reviews Angelia, Chininta Rizka; Nurhayati, Kristina; Amalia, Dinda
Journal of Digital Market and Digital Currency Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i4.45

Abstract

This study conducted a sentiment analysis on 100,000 user reviews of the Kredivo app to assess user satisfaction and identify areas for improvement in the context of digital finance. Leveraging Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction and employing Logistic Regression and Support Vector Machine (SVM) models, the analysis revealed a predominantly positive user sentiment, with 62% of the reviews classified as positive, 25% as negative, and 13% as neutral. Positive reviews frequently highlighted the app's ease of use and quick access to credit, indicating high satisfaction with its functionality and convenience. In contrast, negative reviews commonly cited issues with customer service responsiveness and transparency around fees, suggesting areas where the app could enhance user experience. Visualizations, including a confusion matrix and sentiment distribution charts, further illustrated the model's accuracy and user sentiment patterns. The study’s findings align with previous research in digital finance, which emphasizes the critical role of user feedback in app development and user retention. However, unique insights regarding the challenges faced by buy-now-pay-later (BNPL) platforms like Kredivo were also observed, notably around customer service and fee transparency. The study highlights the potential of sentiment analysis as a tool for digital finance app developers to continuously improve service quality. Limitations include potential biases in the dataset and model limitations, suggesting future research directions that incorporate additional data sources and advanced NLP models.
Sentiment Analysis of User Reviews on Cryptocurrency Trading Platforms Using Pre-Trained Language Models for Evaluating User Satisfaction Javadi, Milad; Sugianto, Dwi; Sarmini
Journal of Digital Market and Digital Currency Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i4.46

Abstract

The study examines user sentiment on the Indodax cryptocurrency trading platform using pre-trained Indonesian language models for sentiment analysis. A dataset of 25,000 user reviews was analyzed, revealing that most reviews expressed neutral sentiment, with positive sentiments accounting for 20% and negative sentiments under 4%. The sentiment classification models used include Support Vector Machine (SVM), Logistic Regression, and Naive Bayes. SVM achieved the highest predictive accuracy at 94.22%, followed by Logistic Regression at 93.62%. These models classified sentiments based on TF-IDF feature extraction, highlighting SVM's effectiveness in sentiment classification within the user reviews. Additionally, sentiment trends over time were analyzed, showing fluctuations in user satisfaction corresponding with market events and platform changes, emphasizing the importance of maintaining platform stability during high volatility. The study’s findings suggest actionable improvements for Indodax, such as addressing user concerns that lead to negative sentiments, like customer service and technical issues, while reinforcing platform strengths, such as ease of use. These insights enable Indodax to enhance user satisfaction and retention by monitoring sentiment trends and adjusting features accordingly. However, the study faces limitations due to the use of pre-trained models that may not fully capture Indonesian language nuances and the absence of demographic data, which limits the analysis to general sentiment trends. Future research could incorporate demographic insights and user behavior metrics to offer a more personalized understanding of user sentiment, ultimately aiding Indodax in delivering a more tailored and satisfying user experience.
Uncovering Key Service Improvement Areas in Digital Finance: A Topic Modeling Approach Using LDA on User Reviews Othman, Jalel Ben; Hariguna, Taqwa
Journal of Digital Market and Digital Currency Vol. 2 No. 4 (2025): Regular Issue December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i4.47

Abstract

The rapid expansion of digital finance has transformed the way financial services are accessed and utilized, particularly in emerging markets such as Indonesia. This study aims to uncover key service improvement areas within the Easycash mobile lending platform by analyzing user reviews through topic modeling using Latent Dirichlet Allocation (LDA). The research employed a data-driven approach, combining text preprocessing in Bahasa Indonesia using the Sastrawi library, TF-IDF vectorization, and sentiment classification with machine learning models including Naive Bayes, K-Nearest Neighbors (KNN), and XGBoost. The XGBoost model achieved the highest performance with an F1-score of 0.9709, effectively distinguishing between positive, neutral, and negative sentiments. LDA analysis identified five major topics: Loan Limits and Repayment, Customer Gratitude and Satisfaction, Loan Application Process and Interest Rates, App Quality and Customer Service, and Data Management and Account Issues. Results indicate that while Easycash users generally express positive sentiment toward ease of use and service speed, concerns persist regarding high interest rates, customer service responsiveness, and data privacy. These findings provide actionable insights for fintech companies to enhance user satisfaction through targeted service improvements and continuous feedback analysis.

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