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ITS » Undergraduate Theses » Matematika
Posted by yeni at 05/12/2008 10:49:01  •  25989 Views


APLIKASI DATA MINING UNTUK MEMPREDIKSI KELAS RESIKO PEMBERIAN KREDIT MENGGUNAKAN SUPPORT VECTOR MACHINE SVM

DATA MINING APPLICATION TO PREDICT CLASS CREDIT GIVING RISK USING SUPPORT VECTOR MACHINE SVM

Author :
Oktrivianto, Rifaldi 




ABSTRAK

Didalam kegiatan perkreditan sering terjadi masalah kredit macet atau kredit bermasalah yang disebabkan oleh gagalnya pengembalian sebagian pinjaman yang diberikan kepada para peminjam. Masalah ini sebenarnya dapat diatasi salah satunya dengan mengidentifikasi dan memprediksi nasabah dengan baik sebelum memberikan pinjaman dengan cara memperhatikan data historis pinjaman. SVM adalah suatu teknik dalam Data Mining yang dapat dipakai untuk melakukan klasifikasi. Dalam teknik SVM untuk masalah klasifikasi k-kelas kita menemukan k fungsi pemisah . Kemudian kelas dari suatu data atau obyek baru kkbZwbZwbZw.... 1. 1 2 . 2 Z ditentukan berdasarkan nilai terbesar dari fungsi pemisah jclass of Z. mmkmb w Z argmax . 12... Hasil yang diperoleh dari Tugas Akhir ini adalah metode SVM dapat digunakan untuk memprediksi calon nasabah baru dengan melakukan pengenalan pola data historis.Hasil dari perangkat lunak pada Tugas akhir ini memberikan hasil prediksi kelas satu pengembalian tepat waktu kelas dua penunggakan tiga bulan kelas tiga penunggakan enam bulan kelas empat penunggakan sembilan bulan dan kelas lima penunggakan dua belas bulan dalam memprediksi kelas resiko pemberian kredit pada calon penerima pinjaman Bank.


ABSTRACT

In Credit activity the problem of doubtful credit is usually appears caused by uncollectible credit given to the debitur. This problem actually can be solved by identifying and predicting well the profile of the customer based on the credit history database before giving the credit. In multi class SVM technique for classifying case k-class we find I k i arbiter where k is the sum of the class we get k function as arbitrator function. . Then the class in new data or object Z can be dicided based on the biggest value from arbiter function kkbZwbZwbZw......2211jclass of Z mmkmbZw.maxarg...21 The result from this final project is SVM method that usable to predict new customer by knowing the pattern of history data. The result from Software of this final project gives the prediction of class 1 returning on timely class 2 3 months overdue class 3 6 months overdue class 4 9 months overdue class 5 12 months overdue to predict class risk in giving the credit to the Bank debitur.



KeywordsData mining; classification; support vector machine
 
Subject:  Pengolahan data elektronis;Matematika bisnis
Contributor
  1. Dr. M. Isa Irawan, MT
Date Create: 05/12/2008
Type: Text
Format: pdf.
Language: Indonesian
Identifier: ITS-Undergraduate-3100008031477
Collection ID: 3100008031477
Call Number: RSMa 006.312 Okt a


Source
Undergraduate Theses,Mathematics, RSMa 006.312 Okt a ,2008

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