Open AccessMODEL PENILAIAN KREDIT MENGGUNAKAN ANALISIS DISKRIMINAN DENGAN VARIABEL BEBAS CAMPURAN BINER DAN KONTINU
Author(s) -
Moch. Abdul Mukid,
Tatik Widiharih
Publication year - 2017
Publication title -
media statistika
Language(s) - English
Resource type - Journals
ISSN - 2477-0647
DOI - 10.14710/medstat.9.2.107-117
Subject(s) - linear discriminant analysis , statistics , actuarial science , marital status , discriminant , econometrics , mathematics , computer science , economics , demography , artificial intelligence , sociology , population
Credit scoring models is an important tools in the credit granting process. These models measure the credit risk of a prospective client. This study aims to applied a discriminant model with mixed predictor variables (binary and continuous) for credit assesment. Implementation of the model use debitur characteristics data from a bank in Lampung Province which the used binary variables involve sex and marital status. Whereas, the continuous variables that was considered appropriate in the model are age, net income, and length of work. By using the data training, it was known that the misclassification of the model is 0.1970 and the misclassification of the testing data reach to 0.3753. Keywords: discriminant analysis, mixed variables, credit scoring