Article Per Year (5 Year)
p-Index From 2020 - 2025
1.048
P-Index
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
Ghaffar, Soeltan Abdul
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Misconceptions of metaverse: from etymology to technology Putawa, Rilliandi Arindra; Izumi, Calvina; Sugianto, Dwi; Ghaffar, Soeltan Abdul
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp314-320

Abstract

The emergence of the metaverse in society is followed by certain confusions, whereas the line between virtual reality and the metaverse remains unclear. Ironically, this has affected the development of the metaverse itself, focusing more on virtual reality while being one of its side components. This has led to the concept losing popularity compared to artificial intelligence technology. This research is a qualitative study that aims to explore the issues at the root of misconceptions and reconstruct the true meaning of the metaverse itself. This research indicates that the misconception already existed when the term was first used alongside virtual reality technology. The term "meta" refers to a higher reality, whereas the terms "digiverse" or "virtuverse" can be used, considering that the terms "digital" and "virtual" can refer to realities lower than the universe.
Uncovering the Efficiency of Phishing Detection: An In-depth Comparative Examination of Classification Algorithms Sugianto, Dwi; Putawa, Rilliandi Arindra; Izumi, Calvina; Ghaffar, Soeltan Abdul
International Journal for Applied Information Management Vol. 4 No. 1 (2024): Regular Issue: April 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i1.72

Abstract

This research aims to investigate the potential security risks associated with phishing email attacks and compare the performance of three main classification algorithms: random forest, SVM, and a combination of k-fold cross-validation with the xgboost model. The dataset consists of 18,634 emails, with 7,312 identified as phishing emails and 11,322 considered safe. Through experiments, the combination of k-fold cross-validation and xgboost demonstrated the best performance with the highest accuracy of 0.9712828770799785. The email classification graph provides a visual insight into the distribution of classification results, aiding in understanding patterns and trends in phishing attack detection. The analysis of the ROC curve results indicates that k-fold cross-validation and xgboost have a higher AUC compared to random forest and SVM, signifying a better ability to predict the correct class. The conclusion emphasizes the importance of the combination of k-fold cross-validation and xgboost in enhancing email security, with the potential for increased accuracy through parameter adjustments.
Comparative Sentiment Analysis of Digital Wallet Applications in Indonesia Using Naïve Bayes Ghaffar, Soeltan Abdul; Setiawan, Wilbert Clarence
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.251

Abstract

The rapid growth of financial technology in Indonesia has led to widespread use of digital wallet applications such as OVO, DANA, GoPay, and ShopeePay. User-generated reviews on platforms like the Google Play Store offer valuable insights into customer satisfaction and application performance. This study conducts a comparative sentiment analysis of user reviews for four major Indonesian e-wallets using the Multinomial Naïve Bayes algorithm. A total of 401 Indonesian-language reviews were collected and labeled based on user ratings, with sentiment classified as positive or negative. The TF-IDF method was applied for feature extraction, and the model was evaluated using accuracy, precision, and recall metrics. Results show that ShopeePay achieved the highest classification accuracy (89%), followed by DANA and GoPay (80%), while OVO recorded lower performance due to more informal and ambiguous language. The model demonstrated strong precision for positive sentiment but low recall for negative sentiment (28%), indicating challenges in detecting minority-class feedback. Word cloud visualizations were used to highlight common keywords in each sentiment category. This study confirms that Naïve Bayes is an effective approach for classifying user sentiment in Indonesian-language app reviews, while also emphasizing the need for improved handling of class imbalance in future research. The findings provide practical insights for developers to enhance user experience based on data-driven sentiment patterns.
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.
Analyzing Price Volatility of Hedera Hashgraph Using GARCH Models: A Data Mining Approach Izumi, Calvina; Setiawan, Wilbert Clarence; Ghaffar, Soeltan Abdul
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.35

Abstract

This study employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to analyze the volatility dynamics of Hedera Hashgraph, a prominent cryptocurrency. Using a dataset of 1,901 daily price observations, we investigate the presence of volatility clustering and the persistence of market shocks, which are hallmarks of financial markets. The GARCH(1,1) model demonstrates robust performance, with a Log-Likelihood of 2927.50, AIC of -5846.99, and BIC of -5824.79, confirming its suitability for volatility estimation. Key findings reveal significant volatility clustering, with alpha (α = 0.20) and beta (β = 0.78) indicating moderate sensitivity to recent shocks and high persistence of volatility, respectively. Visualizations of conditional volatility and historical price data highlight the inverse relationship between price stability and volatility, with high volatility periods accounting for 33% of the dataset. These insights underscore the importance of real-time volatility monitoring for risk management and investment strategies. The study concludes by suggesting future research directions, including the integration of GARCH models with machine learning techniques and the exploration of external factors influencing cryptocurrency price dynamics.
Exploring Thematic Travel Preferences of Global Cities through Agglomerative Hierarchical Clustering for Enhanced Travel Recommendations Ghaffar, Soeltan Abdul; Setiawan, Wilbert Clarence
International Journal for Applied Information Management Vol. 5 No. 4 (2025): Regular Issue: December 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i4.111

Abstract

This study explores the application of Agglomerative Hierarchical Clustering (AHC) to categorize global cities based on thematic travel preferences, aiming to enhance personalized travel recommendations. The dataset used contains travel information for 560 cities worldwide, including thematic ratings across nine categories: culture, adventure, nature, beaches, nightlife, cuisine, wellness, urban, and seclusion, along with climate data and city descriptions. Feature engineering was performed to calculate an overall rating for each city by averaging its thematic scores, and to compute an average annual temperature from monthly climate data. The primary objective of this research was to use AHC to group cities into distinct clusters based on these thematic ratings. The analysis revealed six clusters, each representing different types of travel experiences. Cluster 1 consists of urban cultural hubs with high ratings for culture, cuisine, and urban experiences, while Cluster 2 features cities with a balance of cultural and culinary experiences alongside moderate natural and nightlife attractions. Cluster 3 represents remote, nature-focused cities with high ratings for seclusion and nature. Cluster 4 includes cities renowned for their beaches, nature, and cuisine, while Cluster 5 groups cities that emphasize adventure, nature, and seclusion. Cluster 6 is made up of destinations with a focus on nature, adventure, and seclusion, offering a balance between outdoor activities and tranquility. These findings offer a deeper understanding of the diversity in global city offerings and can significantly improve the effectiveness of travel recommendation systems by aligning cities with users' thematic preferences. By categorizing cities into meaningful clusters, personalized travel suggestions can be made based on users’ specific interests, such as cultural exploration, adventure, or nature. This research lays the groundwork for future studies to incorporate additional data sources and explore alternative clustering techniques for even more refined travel recommendations. The practical applications of this research can enhance real-world travel recommendation platforms, making them more tailored and relevant to individual user preferences
Co-Authors Izumi, Calvina Putawa, Rilliandi Arindra Setiawan, Wilbert Clarence Sugianto, Dwi