Analisis Klasifikasi Kredit Menggunakan Regresi Logistik Biner Dan Radial Basis Function Network di Bank ‘X’ Cabang Kediri

Sistya Rosi Diaprina, Suhartono Suhartono
Submission Date: 2014-08-24 17:56:07
Accepted Date: 2014-09-14 11:50:42

Abstract


Kredit macet merupakan salah satu faktor penyebab terjadinya kebangkrutan pada industri perbankan. Dalam dunia perbankan, diperlukan analisis yang mampu mengurangi terjadinya resiko kredit. Penelitian tugas akhir ini bertujuan untuk menganalisis klasifikasi kredit guna mengurangi resiko terjadinya kredit macet di Bank X Cabang Kediri. Metode statistik yang digunakan adalah Regresi logistik Biner dan Radial Basis Function Network. Tahapan yang digunakan dalam penelitian ini adalah membagi data menjadi dua bagian yaitu data training dan data testing. Data trainingakan digunakan untuk pembentukan model, sedangkan data testing digunakan untuk menguji seberapa besar ketepatan model yang dibentuk. Hasil analisis menunjukan bahwarata-rata ketepatan klasifikasi dengan menggunakan metode Regresi Logistik Biner lebih besar dibandingkan dengan menggunakan metode Radial Basis Function Network. Sehingga dapat disimpulkan bahwa metode Regresi Logistik Binermemiliki ketepatan klasifikasi yang lebih baik dari pada Radial Basis Function Networkuntuk kasus klasifikasi kredit di Bank X Cabang Kediri.

Keywords


Credit ScoringRadial Basis Function Network;Regresi Logistik Biner;Klasifikasi

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