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      "date": "2026-02-25",
      "keywords": "Kelayakan kredit, Soft Voting Ensemble, Logistic Regression,\r\nLightGBM, Klasifikasi, Creditworthiness, Logistic Regression, Classification.",
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      "rev_number": 17,
      "contributors": [
        {
          "name": {
            "given": "I Made Artha",
            "family": "Agastya",
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          "nim": "22.11.4875"
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      "abstract": "Perkembangan teknologi finansial telah mendorong transformasi layanan\r\nkredit dari sistem konvensional menuju platform digital. Perubahan ini\r\nmemungkinkan proses evaluasi kelayakan kredit dilakukan secara lebih efektif dan\r\nterstruktur. Namun, meningkatnya penggunaan layanan kredit digital juga\r\nmenyebabkan lonjakan jumlah pengajuan pinjaman yang harus dianalisis secara\r\nakurat dan konsisten. Pertumbuhan penyaluran kredit yang terus meningkat\r\nmenunjukkan pentingnya sistem evaluasi risiko kredit yang responsif dan mampu\r\nmengelola data dalam skala besar. Ketidaktepatan dalam evaluasi dapat\r\nmeningkatkan risiko kredit bermasalah, seperti gagal bayar, yang berdampak pada\r\nkerugian finansial serta menurunkan kepercayaan terhadap sistem keuangan digital.\r\nPenelitian ini bertujuan mengembangkan model klasifikasi persetujuan\r\nkredit menggunakan metode Soft Voting Ensemble yang mengombinasikan Logistic\r\nRegression dan LightGBM. Logistic Regression digunakan untuk menangkap\r\nhubungan linier antarvariabel secara interpretatif, sedangkan LightGBM\r\nmengidentifikasi pola non-linier dan interaksi fitur. Tahapan penelitian meliputi\r\npreprocessing data, pelatihan model, serta optimasi melalui hyperparameter tuning\r\ndengan pendekatan ensemble untuk meingkatkan performa model.\r\nHasil penelitian menunjukkan bahwa penerapan SMOTE dan\r\nhyperparameter tuning meningkatkan kinerja model dibandingkan baseline. Model\r\nterbaik diperoleh melalui Soft Voting Ensemble (SMOTE + tuning) dengan akurasi\r\n97,08% dan F1-score 97,11%, sedikit lebih tinggi dibandingkan LightGBM\r\ntunggal. Temuan ini menunjukkan bahwa penggabungan Logistic Regression dan\r\nLightGBM melalui metode Soft Voting Ensemble mampu meningkatkan stabilitas\r\ndan performa klasifikasi dalam evaluasi kelayakan kredit. Hasil penelitian ini dapat\r\ndimanfaatkan oleh lembaga keuangan serta menjadi referensi bagi pengembangan\r\nmetode lebih lanjut.",
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