PEMODELAN RESIKO KREDIT DENGAN PENDEKATAN SUPPORT VECTOR MACHINE SUPPORT VECTOR MACHINE APPROACH TO CREDIT RISK
| Main Authors: | , CHRISTINA EVA NURYANI, , Dr. rer.nat. Dedi Rosadi, M.Sc. |
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| Format: | Thesis NonPeerReviewed |
| Terbitan: |
[Yogyakarta] : Universitas Gadjah Mada
, 2012
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| Subjects: | |
| Online Access: |
https://repository.ugm.ac.id/98055/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=54586 |
Daftar Isi:
- This study focuses on classification models using Support Vector Machine approach. This SVM has the advantage of data computation. The computation of finite data with complexity of the variables can be done using SVM. SVM for classification was applied in credit risk management. Classification was applied to separate the credit application of the client of a particular Bank Perkreditan Rakyat (BPR) into two classes, �good� and �bad�. A particular bank, which classifies the credit application correctly, can minimize the risk of bankruptcy and gain the society trust. By applying the classification, banks can prevent the problems appear to default credit.