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      "abstract": "Penelitian ini membahas perbandingan empat algoritma machine learning, \r\nyaitu Logistic Regression, Random Forest, LightGBM, dan CatBoost pada\r\nklasifikasi kelayakan pinjaman nasabah menggunakan Loan Approval Prediction\r\nDataset dari Kaggle. Permasalahan utama pada penelitian ini adalah menentukan\r\nalgoritma yang paling efektif untuk memprediksi status persetujuan pinjaman,\r\nsekaligus memastikan bahwa rancangan eksperimen terbebas dari data leakage dan\r\ntidak menunjukkan overfitting yang berlebihan. Tahapan penelitian meliputi\r\npembersihan data, rekayasa fitur, exploratory data analysis, pembagian data secara\r\nstratified ke dalam subset train, validasi, dan uji, pelatihan model baseline, serta\r\nfine tuning hyperparameter. Evaluasi model dilakukan menggunakan metrik\r\naccuracy, precision, recall, F1-score, dan ROC-AUC, serta analisis gap performa\r\nantara data latih, validasi, dan data uji. Hasil penelitian menunjukkan bahwa\r\nCatBoost hasil fine tuning memberikan performa terbaik dengan accuracy 0,9977,\r\nF1-score 0,9981, dan ROC-AUC 1,0000 pada data uji. Random Forest menempati\r\nposisi berikutnya, diikuti oleh LightGBM dan Logistic Regression. Tingginya\r\nakurasi model terutama dipengaruhi oleh karakter dataset yang sangat mudah\r\ndipisahkan, terutama karena dominasi variabel cibil_score. Penelitian ini\r\nmemberikan kontribusi berupa komparasi model yang disusun secara metodologis\r\nlebih ketat dan dapat dimanfaatkan sebagai referensi akademik maupun praktis\r\npada pengembangan sistem prediksi kelayakan pinjaman berbasis data."
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