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      "department": "Fakultas Ilmu Komputer",
      "keywords": "Deteksi Fraud, Ensemble Machine Learning, Random Forest, \r\nXGBoost, Data Imbalanced, Fraud Detection,  Imbalanced Data.",
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      "date": "2025-12-16",
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          "name": {
            "family": "Al Asqolani",
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            "given": "Muhammad Alghifari"
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      "abstract": "Penelitian ini bertujuan mendeteksi fraud dengan teknik ensemble machine \r\nlearning, khususnya Random Forest (RF) dan Extreme Gradient Boosting\r\n(XGBoost), diterapkan pada dataset Credit Card Fraud Detection Kaggle (284.807\r\nsampel, 0,17% fraud). Proses metodologi mencakup preprocessing melalui\r\nnormalisasi dan Synthetic Minority Over-sampling Technique (SMOTE) untuk\r\nmenangani ketidakseimbangan, diikuti pelatihan model menggunakan Scikit-learn\r\nserta XGBoost di Python. RF efektif mencegah overfitting lewat bagging,\r\nsementara XGBoost unggul dalam optimasi gradient untuk data skala besar.\r\nEvaluasi dilakukan dengan metrik Area Under the Curve (AUC-ROC), precision,\r\nrecall, dan F1-score melalui cross-validation. Hasil menunjukkan XGBoost lebih\r\nsuperior (AUC-ROC 0,98; precision 0,95; recall 0,92; F1-score 0,93) dibanding RF\r\n(0,96; 0,92; 0,89; 0,90), berkat ketangguhannya terhadap noise. Penelitian\r\nmenyimpulkan XGBoost sebagai metode optimal untuk data imbalanced, dengan\r\nsaran integrasi SMOTE guna tingkatkan recall hingga 15%. Kontribusi teoritis\r\nmemperkaya literatur machine learning di keamanan siber (Al-Hashedi &\r\nMagalingam, 2021), sementara manfaat praktisnya mengurangi kerugian bagi bank\r\ndan fintech, serta mendukung pengembangan model real-time.",
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      "title": "DETEKSI KECURANGAN KEUANGAN DENGAN \r\nMENGGUNAKAN METODE RANDOM FOREST DAN\r\nXGBOOST PADA DATA YANG TIDAKSEIMBANG",
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