| dc.contributor |
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| dc.contributor |
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| dc.creator |
Yunindya, Rifani |
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| dc.creator |
Kudus, Abdul |
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| dc.creator |
Yanti, Teti Sofia |
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| dc.date |
2017-08-09 |
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| dc.identifier |
http://karyailmiah.unisba.ac.id/index.php/statistika/article/view/7608 |
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| dc.description |
Credit scoring is a tool and prediction technique that helps financial institutions to lend. The purpose of credit scoring is to assign prospective customers or customers to one group of "good customer" or "bad customer ".One method that can be used to evaluate credit scoring is Classification and Regression Trees (CART).Classification and Regression Trees (CART) is a statistical method used to perform classification analysis. This paper discusses how to model credit scoring using the Classification and Regression Trees (CART) method.The calculation of credit scoring data is based on the credit history data of the customer.In this paper the data used are credit card customer payment data from April 2005 to September 2005 in Taiwan. The influential independent variables amount of bill statement in April (X17), amount paid in May, 2005 (X22), the repayment status in May, 2005 (X10), the repayment status in July, 2005 (X20), the repayment status in Agusuts, 2005 (X7) And the repayment status in September, 2005 (X6).In this method the classification of credit customers by Classification and Regression Trees (CART) method gives 78.4 percent classification accuracy for training data and 78.6 percent for data testing. |
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| dc.description |
Credit scoring merupakan suatu alat dan teknik prediksi yang membantu lembaga keuangan dalam pemberian kredit. Tujuan dari credit scoring yaitu untuk menetapkan calon nasabah atau nasabah ke salah satu kelompok yaitu “nasabah yang baik” atau “nasabah macet”. Salah satu metode yang dapat digunakan untuk mengevaluasi credit scoring yaitu Classification and Regression Trees (CART). Classification and Regression Trees (CART) adalah metode statistik yang digunakan untuk melakukan analisis klasifikasi. Makalah ini membahas cara memodelkan credit scoring dengan menggunakan metode Classification and Regression Trees (CART). Perhitungan data credit scoring didasarkan pada data riwayat pembayaran kredit nasabah. Dalam makalah ini data yang digunakan adalah data pembayaran pelanggan kartu kredit pada bulan April 2005 sampai dengan bulan September 2005 di Taiwan. Variabel bebas yang berpengaruh adalah jumlah tagihan bulan April 2005 (X17), jumlah pembayaran bulan Mei 2005 (X22), riwayat pembayaran bulan Mei 2005 (X10), jumlah pembayaran bulan Juli 2005 (X20), riwayat pembayaran bulan Agusuts 2005 (X7), dan riwayat pembayaran bulan September 2005 (X6). Dalam metode ini pengklasifikasian nasabah kredit dengan metode Classification and Regression Trees (CART) menghasilkan ketepatan klasifikasi sebesar 78.4 persen untuk data training dan 78.6 persen untuk data testing. |
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| dc.format |
application/pdf |
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| dc.language |
ind |
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| dc.publisher |
Universitas islam Bandung |
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| dc.relation |
http://karyailmiah.unisba.ac.id/index.php/statistika/article/view/7608/pdf |
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| dc.rights |
Copyright (c) 2017 Prosiding Statistika |
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| dc.source |
Prosiding Statistika; Vol 3, No 2, Prosiding Statistika (Agustus, 2017); 68-77 |
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| dc.source |
Prosiding Statistika; Vol 3, No 2, Prosiding Statistika (Agustus, 2017); 68-77 |
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| dc.source |
2460-6456 |
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| dc.subject |
Statistics |
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| dc.subject |
Credit scoring, Classification and Regression Trees, Credit card |
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| dc.subject |
Statistika |
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| dc.subject |
Credit scoring, Classification and Regression Trees, Kartu Kredit |
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| dc.title |
Model Credit Scoring Menggunakan Metode Classification and Regression Trees (CART) pada Data Kartu Kredit |
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| dc.title |
Model Credit Scoring Menggunakan Metode Classification and Regression Trees (CART) pada Data Kartu Kredit |
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| dc.type |
info:eu-repo/semantics/article |
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| dc.type |
info:eu-repo/semantics/publishedVersion |
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| dc.type |
Peer-reviewed Article |
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| dc.type |
Quantitative |
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| dc.type |
Kuantitatif |
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