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This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) IJIIS: International Journal of Informatics and Information Systems International Journal for Applied Information Management Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Sugianto, Dwi
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Journal : Journal of Digital Market and Digital Currency

 1 
Temporal Patterns in User Conversions: Investigating the Impact of Ad Scheduling in Digital Marketing Pratama, Satrya Fajri; Sugianto, Dwi
Journal of Digital Market and Digital Currency Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i2.10

Abstract

This study explores the impact of ad scheduling on user conversions by analyzing temporal patterns in user behavior. In the increasingly competitive landscape of digital marketing, optimizing the timing of ad placements is critical for maximizing user engagement and conversion rates. Utilizing a comprehensive dataset from Kaggle, which includes variables such as user ID, ad exposure details, and conversion outcomes, we employed both time series analysis and survival analysis to uncover insights into how different ad scheduling strategies affect conversion rates. The ARIMA model, used for time series analysis, provided reasonable predictive accuracy with a Mean Absolute Error (MAE) of 389.92, Root Mean Squared Error (RMSE) of 463.97, and Mean Absolute Percentage Error (MAPE) of 2.26%. This model effectively identified specific hours and days with higher likelihoods of conversion, particularly during evenings and weekends. On the other hand, the Cox Proportional Hazards model, used for survival analysis, demonstrated superior performance with a concordance index of 0.97, indicating its exceptional ability to predict the timing of user conversions based on various covariates such as the number of ads seen and the specific hours of exposure. The findings suggest that strategic ad scheduling, tailored to align with user temporal behavior, can significantly enhance marketing effectiveness by targeting users during peak conversion periods. These insights offer practical implications for digital marketers aiming to refine their ad delivery strategies to achieve higher conversion rates and improve return on investment.
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.
Co-Authors Ghaffar, Soeltan Abdul Hananto, Andhika Rafi Izumi, Calvina Javadi, Milad Pratama, Satrya Fajri Putawa, Rilliandi Arindra Sarmini Tri Wahyuningsih Yulianto, Koko Edy