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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. 1 (2025): Regular Issue March" : 5 Documents clear
Volatility and Risk Assessment of Blockchain Cryptocurrencies Using GARCH Modeling: An Analytical Study on Dogecoin, Polygon, and Solana Doan, Minh Luan
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i1.25

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.
Enhancing Customer Satisfaction and Product Quality in E-commerce through Post-Purchase Analysis using Text Mining and Sentiment Analysis Techniques in Digital Marketing Izumi, Calvina; Ghaffar, Soeltan Abdul; Setiawan, Wilbert Clarence
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i1.26

Abstract

This study explores the application of text mining and sentiment analysis to enhance product quality and customer satisfaction within the e-commerce landscape. Using the Customer360Insights dataset, which comprises 236 records of customer interactions, demographic details, product information, and transactional data, we identified key drivers of negative feedback and returns. The descriptive statistics revealed a diverse customer base with an average age of 45.33 years and significant variability in monthly income ($5,470.24 ± $1,442.80). The text mining process, including tokenization and term frequency analysis, identified frequent terms such as "poor" (95 occurrences), "arrived" (92 occurrences), and "damaged" (45 occurrences). Sentiment analysis using VADER and TextBlob indicated that 80.08% of the feedback was negative, highlighting pervasive dissatisfaction. Topic modeling using Latent Dirichlet Allocation (LDA) revealed five main topics, consistently emphasizing issues like product quality and delivery timeliness. Common return reasons included poor value (55 occurrences), wrong item delivered (49 occurrences), and late arrivals (47 occurrences). These insights suggest critical areas for improvement, such as enhancing quality control, optimizing logistics, and refining pricing strategies. The findings have significant implications for digital marketing strategies, emphasizing the need for targeted interventions to improve customer satisfaction. By addressing identified issues and leveraging data-driven insights, e-commerce businesses can enhance their product offerings, optimize post-purchase support, and foster customer loyalty. Future research should validate these findings using real-world data and explore additional data mining techniques to provide a comprehensive understanding of customer satisfaction drivers.
Evaluating the Effectiveness of Digital Marketing Campaigns through Conversion Rates and Engagement Levels Using ANOVA and Chi-Square Tests Rahardja, Untung; Aini, Qurotul
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i1.27

Abstract

This study investigates the effectiveness of various digital marketing campaign types—Awareness, Conversion, and Retention—on conversion rates and engagement levels. Using a dataset of 8,000 records, we conducted a comprehensive analysis through ANOVA, Chi-Square tests, and OLS regression to understand the impact of these campaign types. The ANOVA results indicated no significant differences in conversion rates across the campaign types, with an F-statistic of 0.4752 and a p-value of 0.6218. Similarly, the analysis for engagement levels, measured by website visits, yielded an F-statistic of 0.3651 and a p-value of 0.6942, suggesting no significant differences among the campaigns. Despite these findings, the Chi-Square test revealed a significant association between campaign types and conversion outcomes, with a Chi-Square statistic of 84.4544 and a p-value of approximately 3.3983e-18. This suggests that while the overall conversion rates do not differ significantly, the type of campaign does influence whether conversions occur. Pairwise t-tests supported these results, showing no significant differences in conversion rates or engagement levels between specific pairs of campaign types. Further, OLS regression analysis for conversion rates resulted in an R-squared value of 0.001 and a non-significant F-statistic, indicating that the predictors such as AdSpend and ClickThroughRate do not significantly explain the variation in conversion rates. Similarly, the regression model for engagement levels, despite an R-squared value of 1.000, highlighted issues of multicollinearity and overfitting. These findings imply that simply altering the type of campaign may not substantially impact conversion rates or engagement levels. Marketers should focus on improving content quality, targeting precision, and user experience to enhance campaign effectiveness. Future research should incorporate additional variables and advanced modeling techniques to provide deeper insights into the factors driving digital marketing success.
Optimizing Pricing Strategies for Female Fashion Products Using Regression Analysis to Maximize Revenue and Profit in Digital Marketing Siddique, Quba
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i1.28

Abstract

This study explores optimal pricing strategies for the female fashion sector through the application of advanced data science methodologies. Utilizing a dataset of 4,272 entries, comprising various attributes such as original prices, promotional prices, and discount percentages, we employed regression models to predict promotional pricing. The research highlights Ridge Regression as the most effective model, balancing high accuracy with reduced overfitting. The model achieved an R-squared (R²) value of 0.9999999999999678, a Mean Absolute Error (MAE) of 4.31×10−6, and a Mean Squared Error (MSE) of 4.89×10 −11, demonstrating its robustness and reliability. The study's findings indicate that dynamic pricing and tailored discount strategies can significantly enhance revenue and profitability. High-value items are best priced with moderate discounts, maintaining higher promotional prices, while low-value items benefit from aggressive discounting to drive sales volume. Sensitivity analysis further supported these strategies by showing that a 10% increase in original prices proportionally increased promotional prices, while a 10% increase in discount percentages led to lower promotional prices, affecting sales performance differently across product categories. Practical implications for e-commerce businesses include implementing dynamic pricing, developing targeted discount strategies, and timing promotions strategically. Regular sensitivity analysis and continuous model validation are recommended to adapt to market changes effectively. Future research should consider broader datasets, advanced modeling techniques, external market factors, and customer segmentation to enhance the generalizability and applicability of pricing strategies across different sectors. This research underscores the importance of data-driven approaches in optimizing digital marketing strategies, offering actionable insights that can significantly boost revenue and profitability in the female fashion sector.
Predictive Analysis for Optimizing Targeted Marketing Campaigns in Bike-Sharing Systems Using Decision Trees, Random Forests, and Neural Networks Warmayana, I Gede Agus Krisna; Yamashita, Yuichiro; Oka, Nobuta
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i1.29

Abstract

This research explores the use of machine learning models to predict bike rental demand and optimize targeted marketing campaigns in bike-sharing systems. Utilizing the day.csv and hour.csv datasets, which provide daily and hourly bike rental data, we implemented Decision Tree Regressor, Random Forest Regressor, and Neural Networks (MLPRegressor) to forecast demand. The Random Forest model outperformed the others, achieving an RMSE of 709.08 and an MAE of 469.99 for daily predictions, while the Neural Network demonstrated potential for hourly forecasts. Our analysis revealed significant trends, including increased demand during summer months and peak usage times on weekday mornings and evenings, highlighting the importance of temporal and weather-related factors in predicting bike rental demand. The study's predictive insights allow bike-sharing companies to enhance operational efficiency by optimizing bike allocation during peak periods and reducing idle capacity during off-peak times. Furthermore, the ability to predict demand accurately enables the development of data-driven marketing strategies, such as launching promotions during high-demand periods and targeting specific user groups based on rental patterns. Despite the promising results, challenges such as data preprocessing complexities and computational resource constraints were encountered. Additionally, the study's scope was limited by the available data, suggesting the need for future research to incorporate additional data sources, like real-time traffic conditions and social events, and to explore more advanced machine learning techniques to further improve prediction accuracy. In conclusion, this research underscores the value of predictive analytics in optimizing bike-sharing systems and marketing strategies, contributing to more efficient and user-centric urban mobility solutions.

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