CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS

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...

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Main Authors: , MUHAMAD RASHIF HILMI, , Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc.
Format: Thesis
Published: [Yogyakarta] : Universitas Gadjah Mada 2014
Subjects:
ETD
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author , MUHAMAD RASHIF HILMI
, Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc.
author_facet , MUHAMAD RASHIF HILMI
, Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc.
author_sort , MUHAMAD RASHIF HILMI
collection UGM
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.
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spelling oai:generic.eprints.org:1318592016-03-04T08:00:37Z https://repository.ugm.ac.id/131859/ CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS , MUHAMAD RASHIF HILMI , Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. ETD 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. [Yogyakarta] : Universitas Gadjah Mada 2014 Thesis NonPeerReviewed , 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. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=72368
spellingShingle ETD
, MUHAMAD RASHIF HILMI
, Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc.
CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS
title CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS
title_full CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS
title_fullStr CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS
title_full_unstemmed CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS
title_short CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS
title_sort credit scoring adaptif menggunakan kernel learning methods
topic ETD
work_keys_str_mv AT muhamadrashifhilmi creditscoringadaptifmenggunakankernellearningmethods
AT profdrrernatdedirosadissimsc creditscoringadaptifmenggunakankernellearningmethods