Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Journal of Digital Market and Digital Currency International Journal Research on Metaverse
Soeltan Abdul Ghaffar
Department of Marine Information Systems, Universitas Pendidikan Indonesia, Bandung, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Enhancing Customer Satisfaction and Product Quality in E-commerce through Post-Purchase Analysis using Text Mining and Sentiment Analysis Techniques in Digital Marketing Calvina Izumi; Soeltan Abdul Ghaffar; Wilbert Clarence Setiawan
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March 2025
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.
Metaverse Dynamics: Predictive Modeling of Roblox Stock Prices using Time Series Analysis and Machine Learning Soeltan Abdul Ghaffar; Wilbert Clarence Setiawan
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.6

Abstract

Stock price prediction is a critical task in finance and investment, enabling investors to make informed decisions and capitalize on market opportunities. This paper explores the application of predictive modeling techniques to forecast the stock prices of Roblox Corporation, a prominent player in the gaming industry. Despite the growing interest in predictive analytics, there remains a research gap concerning the application of these techniques to specific companies, particularly within the gaming sector. To address this gap, we employ a comprehensive dataset spanning from March 2021 to June 2023, obtained from Yahoo Finance, to develop predictive models using both time series analysis and machine learning algorithms. Our analysis encompasses exploratory data analysis, model development, and evaluation, culminating in insights into Roblox's stock price dynamics and model performance. The evaluation of our predictive models reveals promising results, with a Mean Squared Error (MSE) of 1.22, Root Mean Squared Error (RMSE) of 1.10, and a high R-squared (R2) score of 0.998. These metrics indicate relatively low prediction errors and a strong explanatory power of the models in capturing the variance in Roblox's closing prices. The findings shed light on the unique challenges and opportunities in predicting stock prices within the gaming industry and contribute to the growing body of knowledge in finance and investment. Through our research endeavors, we aim to empower investors and stakeholders with actionable insights to navigate the complexities of financial markets and make informed decisions with confidence and agility.
Co-Authors Calvina Izumi Wilbert Clarence Setiawan Wilbert Clarence Setiawan