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 33 Documents
 1   2   3   4 
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.

Page 4 of 4 | Total Record : 33

 1   2   3   4 

Filter by Year

2024 2025


Reset
Filter By Issues
All Issue 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