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      "department": "Fakultas Ilmu Komputer",
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      "keywords": "Credit Card Fraud Detection, Deep Learning, Stacking Ensemble,\r\nMachine Learning, SMOTEENN, Imbalanced Dataset.",
      "thesis_type": "bachelor",
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      "abstract": "Penelitian ini bertujuan mengevaluasi kinerja model deep learning dan machine\r\nlearning, serta mengkaji efektivitas stacking ensemble dengan Multi-Layer\r\nPerceptron (MLP) sebagai meta-learner dalam mendeteksi penipuan kartu kredit\r\npada dataset dengan ketidakseimbangan kelas tinggi. Model yang diuji meliputi\r\nLSTM (Long Short-Term Memory) dan GRU (Gated Recurrent Unit) untuk\r\nmenangkap pola temporal, serta Random Forest dan AdaBoost sebagai pembanding\r\nberbasis decision tree. Dataset diambil dari platform Kaggle yang telah melalui\r\nPrincipal Component Analysis (PCA) pada fitur V1–V28, ditambah fitur asli Time\r\ndan Amount. Tahapan penelitian meliputi eksplorasi data, analisis korelasi, deteksi\r\noutlier dan skewness, penghapusan duplikasi, serta penyeimbangan kelas\r\nmenggunakan undersampling dan SMOTEENN. Model dilatih dan diuji\r\nmenggunakan metrik akurasi, precision, recall, dan F1-score, serta dievaluasi\r\ndengan confusion matrix, ROC curve, dan Precision-Recall curve. Hasil pengujian\r\nmenunjukkan GRU unggul dengan akurasi 98,42%, precision 98,95%, recall\r\n97,93%, dan F1-score 98,43%, melampaui LSTM yang mencatat akurasi 96,85%.\r\nRandom Forest sedikit lebih baik daripada AdaBoost (97,38% vs 97,12%) dalam\r\nkestabilan pada data tidak seimbang. Stacking ensemble menghasilkan akurasi ratarata\r\n\r\n98,17% dan meningkatkan stabilitas prediksi meski tidak secara signifikan\r\nmengungguli GRU. Kesimpulannya, GRU direkomendasikan sebagai model utama\r\nkarena akurasi dan kestabilannya, sementara stacking ensemble dapat menjadi\r\nalternatif untuk meningkatkan konsistensi hasil. Random Forest dan AdaBoost\r\ntetap relevan sebagai baseline yang ringan secara komputasi. Temuan ini\r\nmemberikan kontribusi praktis bagi pengembangan sistem deteksi fraud yang\r\nakurat, stabil, dan adaptif terhadap variasi pola transaksi dalam industri perbankan\r\ndan layanan pembayaran digital",
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            "honourific": null,
            "given": "Muhammad Aditya"
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        }
      ],
      "date_type": "published",
      "title": "ANALISIS KOMPARATIF STACKING ENSEMBLE DEEP\r\nLEARNING DAN MACHINE LEARNING UNTUK DETEKSI\r\nKECURANGAN KARTU KREDIT PADA DATASET TIDAK\r\nSEIMBANG",
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      "institution": "Universitas AMIKOM Yogyakarta"
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