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        <dc:title>EVALUASI KOMPARATIF LOGISTIC REGRESSION, LIGHTGBM, DAN FT-TRANSFORMER UNTUK PREDIKSI RISIKO KREDIT: VALIDASI BERBASIS UJI STATISTIK NON-PARAMETRIK DAN INTERPRETABILITAS SHAP</dc:title>
        <dc:creator>Firda Maulida Prayetno, NIM.: 24206051020</dc:creator>
        <dc:subject>006.3 Artificial Intelligence (Kecerdasan Buatan)</dc:subject>
        <dc:description>Penelitian ini melakukan evaluasi komparatif antara Regresi Logistik, LightGBM, dan FT-Transformer dalam memprediksi risiko gagal bayar menggunakan dataset Give Me Some Credit (150.000 sampel) dengan tingkat ketidakseimbangan kelas ekstrem (1:13,96). Guna mengatasi bias data, teknik Synthetic Minority Over-sampling Technique (SMOTE) diterapkan secara lokal dalam skema 15-fold Stratified Cross-Validation, dengan optimasi hiperparameter berbasis framework Optuna. Hasil pengujian menunjukkan LightGBM mencapai performa superior dengan AUC-ROC akhir sebesar 0,8631, mengungguli FT-Transformer (0,8369) dan Regresi Logistik (0,7959). Validasi inferensial melalui Friedman Test (</dc:description>
        <dc:date>2026-08-12</dc:date>
        <dc:type>Thesis</dc:type>
        <dc:type>NonPeerReviewed</dc:type>
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        <dc:identifier>https://digilib.uin-suka.ac.id/id/eprint/78783/1/24206051020_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf</dc:identifier>
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        <dc:identifier>https://digilib.uin-suka.ac.id/id/eprint/78783/2/24206051020_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf</dc:identifier>
        <dc:identifier>  Firda Maulida Prayetno, NIM.: 24206051020  (2026) EVALUASI KOMPARATIF LOGISTIC REGRESSION, LIGHTGBM, DAN FT-TRANSFORMER UNTUK PREDIKSI RISIKO KREDIT: VALIDASI BERBASIS UJI STATISTIK NON-PARAMETRIK DAN INTERPRETABILITAS SHAP.  Masters thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   </dc:identifier></oai_dc:dc>
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