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 2 Documents
Search
Journal : International Journal for Applied Information Management

 1 
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.
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