Machine Learning Algorithms: Detection Official Hajj and Umrah Travel Agency Based on Text and Metadata Analysis
Wisnu Uriawan, Muhamad Veva Ramadhan, Firman Adi Nugraha, Hasbi Nur Wahid, M Dantha Arianvasya, Muhammad Zaki Alghifari

TL;DR
This study develops and evaluates machine learning models, particularly SVM, to automatically verify the authenticity of Hajj and Umrah travel apps in Indonesia, aiming to combat digital fraud and protect user data.
Contribution
It introduces a hybrid feature extraction approach combining textual and metadata analysis for app verification, achieving high accuracy and providing a scalable solution for digital trust enhancement.
Findings
SVM achieved 92.3% accuracy in app verification.
Keywords related to legality and permissions are key discriminators.
The system can serve as a prototype for a national verification platform.
Abstract
The rapid digitalization of Hajj and Umrah services in Indonesia has significantly facilitated pilgrims but has concurrently opened avenues for digital fraud through counterfeit mobile applications. These fraudulent applications not only inflict financial losses but also pose severe privacy risks by harvesting sensitive personal data. This research aims to address this critical issue by implementing and evaluating machine learning algorithms to verify application authenticity automatically. Using a comprehensive dataset comprising both official applications registered with the Ministry of Religious Affairs and unofficial applications circulating on app stores, we compare the performance of three robust classifiers: Support Vector Machine (SVM), Random Forest (RF), and Na"ive Bayes (NB). The study utilizes a hybrid feature extraction methodology that combines Textual Analysis (TF-IDF) of…
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Taxonomy
TopicsSpam and Phishing Detection · Text and Document Classification Technologies · Web Application Security Vulnerabilities
