用机器学习识别假朝觐应用，准确率达92.3%
Machine Learning Algorithms: Detection Official Hajj and Umrah Travel Agency Based on Text and Metadata Analysis
- 结合文本与权限信息，用SVM分类器判断应用真伪
- 准确率92.3%，关键特征包括合法关键词和高危权限
- 适合监管机构构建官方应用验证系统
印度尼西亚朝觐与副朝服务的数字化快速发展虽便利了朝觐者，但也为假冒移动应用带来的数字欺诈创造了机会。这些虚假应用不仅造成财务损失，还通过窃取敏感个人信息带来严重隐私风险。本研究旨在通过实施并评估机器学习算法，实现对应用真实性的自动验证。基于包含宗教事务部注册的官方应用及应用商店中流通的非官方应用的综合数据集，对比了支持向量机（SVM）、随机森林（RF）和朴素贝叶斯（NB）三种分类器的表现。采用融合文本分析（TF-IDF）与元数据（敏感权限）的混合特征提取方法。实验结果表明，SVM算法表现最优，准确率为92.3%，精确率为91.5%，F1得分为92.0%。详细特征分析显示，与合法性相关的关键词以及高风险权限（如READ PHONE STATE）是最重要的区分特征。该系统可作为提升宗教旅游领域数字信任的主动、可扩展解决方案，或成为国家级验证系统的原型。
原文摘要 · Abstract (English)
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 application descriptions with Metadata Analysis of sensitive access permissions. The experimental results indicate that the SVM algorithm achieves the highest performance with an accuracy of 92.3%, a precision of 91.5%, and an F1-score of 92.0%. Detailed feature analysis reveals that specific keywords related to legality and high-risk permissions (e.g., READ PHONE STATE) are the most significant discriminators. This system is proposed as a proactive, scalable solution to enhance digital trust in the religious tourism sector, potentially serving as a prototype for a national verification system.
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