CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS
| Main Authors: | , MUHAMAD RASHIF HILMI, , Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. |
|---|---|
| Format: | Thesis NonPeerReviewed |
| Terbitan: |
[Yogyakarta] : Universitas Gadjah Mada
, 2014
|
| Subjects: | |
| Online Access: |
https://repository.ugm.ac.id/131859/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=72368 |
| ctrlnum |
131859 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><relation>https://repository.ugm.ac.id/131859/</relation><title>CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS</title><creator>, MUHAMAD RASHIF HILMI</creator><creator>, Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc.</creator><subject>ETD</subject><description>Credit scoring is a method based on statistical analysis that used to measure the
amount of credit risk. The most popular methods of classification adopted in the
credit scoring industry are linear discriminant analysis and logistic regression.
However, the method has some limitations. Those methods require the selection of
variables for logistic regression and also the data must follow a certain
distribution for linear discriminant analysis. Based on that information, it is
difficult to automate the process of data modeling occurs when the environment or
a population changes. Kernel method is one of the solutions to these problems.
This method does not require effort and variable selection can always converge to
the optimal solutions and provide the same results without encountering
numerical problems or losing information. It enables modelers to design a credit
scoring process dynamically in practice where decision model can be updated
and improved with the arrival of new information.</description><publisher>[Yogyakarta] : Universitas Gadjah Mada</publisher><date>2014</date><type>Thesis:Thesis</type><type>PeerReview:NonPeerReviewed</type><identifier> , MUHAMAD RASHIF HILMI and , Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. (2014) CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS. UNSPECIFIED thesis, UNSPECIFIED. </identifier><relation>http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=72368</relation><recordID>131859</recordID></dc>
|
| format |
Thesis:Thesis Thesis PeerReview:NonPeerReviewed PeerReview |
| author |
, MUHAMAD RASHIF HILMI , Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. |
| title |
CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS |
| publisher |
[Yogyakarta] : Universitas Gadjah Mada |
| publishDate |
2014 |
| topic |
ETD |
| url |
https://repository.ugm.ac.id/131859/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=72368 |
| contents |
Credit scoring is a method based on statistical analysis that used to measure the
amount of credit risk. The most popular methods of classification adopted in the
credit scoring industry are linear discriminant analysis and logistic regression.
However, the method has some limitations. Those methods require the selection of
variables for logistic regression and also the data must follow a certain
distribution for linear discriminant analysis. Based on that information, it is
difficult to automate the process of data modeling occurs when the environment or
a population changes. Kernel method is one of the solutions to these problems.
This method does not require effort and variable selection can always converge to
the optimal solutions and provide the same results without encountering
numerical problems or losing information. It enables modelers to design a credit
scoring process dynamically in practice where decision model can be updated
and improved with the arrival of new information. |
| id |
IOS2744.131859 |
| institution |
Universitas Gadjah Mada |
| institution_id |
19 |
| institution_type |
library:university library |
| library |
Perpustakaan Pusat Universitas Gadjah Mada |
| library_id |
488 |
| collection |
UGM Repository |
| repository_id |
2744 |
| subject_area |
Karya Umum |
| city |
SLEMAN |
| province |
DAERAH ISTIMEWA YOGYAKARTA |
| repoId |
IOS2744 |
| first_indexed |
2016-09-14T18:34:17Z |
| last_indexed |
2016-09-22T21:48:24Z |
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dc |
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1765816550092177408 |
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17.538404 |