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      "keywords": "XGBoost, Kalibrasi probabilitas, Hybrid calibration ensemble, Brier\r\nScore, Pemasaran bank, Probability calibration, Banking marketing.",
      "subjects": [
        "000.000.000A",
        "000.000.004"
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      "institution": "Universitas AMIKOM Yogyakarta",
      "rev_number": 16,
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      "abstract": "Permasalahan utama dalam pemanfaatan algoritma machine learning pada \r\nbidang pemasaran bank adalah rendahnya reliabilitas probabilitas prediksi,\r\nkhususnya pada dataset yang tidak seimbang. Model XGBoost yang umum\r\ndigunakan cenderung menghasilkan probabilitas yang tidak merepresentasikan\r\npeluang kejadian aktual secara akurat atau mengalami miscalibration. Kondisi ini\r\nberdampak pada kualitas pengambilan keputusan berbasis risiko, seperti penentuan\r\nprioritas nasabah dalam kampanye pemasaran, yang berpotensi menimbulkan\r\ninefisiensi dan kesalahan strategi bisnis. \r\nPenelitian ini bertujuan untuk mengatasi permasalahan miscalibration\r\ntersebut melalui penerapan Hybrid Calibration Ensemble pada model XGBoost.\r\nMetode yang digunakan meliputi tahapan pra-pemrosesan data, pelatihan model\r\nXGBoost baseline, pemisahan data menjadi data pelatihan, kalibrasi, dan pengujian,\r\nserta penerapan metode kalibrasi Platt Scaling, Isotonic Regression, dan Beta\r\nCalibration. Selanjutnya, hasil dari masing-masing metode kalibrasi digabungkan\r\ndalam suatu skema ensemble untuk menghasilkan probabilitas akhir yang lebih\r\nreliabel. Evaluasi kinerja dilakukan menggunakan metrik Brier Score dan analisis\r\nreliability diagram. \r\nHasil penelitian menunjukkan bahwa model XGBoost baseline mengalami\r\nmiscalibration yang signifikan. Metode kalibrasi tunggal mampu memperbaiki\r\nreliabilitas probabilitas, namun peningkatannya masih terbatas. Hybrid Calibration\r\nEnsemble menghasilkan performa terbaik dengan nilai Brier Score terendah dan\r\nkurva reliability diagram yang paling mendekati garis ideal. Penelitian ini\r\nmemberikan kontribusi dalam peningkatan kejujuran probabilitas model machine\r\nlearning dan dapat dimanfaatkan oleh praktisi perbankan, analis data, serta\r\npengambil keputusan dalam sistem pemasaran berbasis risiko. Penelitian\r\nselanjutnya dapat mengembangkan pendekatan ini pada skenario multi-kelas atau\r\ndata dengan perubahan distribusi.",
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      "title": "EVALUASI RELIABILITAS MODEL XGBOOST\r\nMENGGUNAKAN METODE HYBRID CALIBRATION\r\nENSEMBLE PADA DATASET PEMASARAN BANK"
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