Jurnal Gaussian
Vol 5, No 3 (2016): Jurnal Gaussian

ANALISIS DISKRIMINAN FISHER POPULASI GANDA UNTUK KLASIFIKASI NASABAH KREDIT

Ungu Siwi Maharunti (Unknown)
Moch. Abdul Mukid (Unknown)
Agus Rusgiyono (Unknown)



Article Info

Publish Date
30 Aug 2016

Abstract

Credit is the biggest asset carried out by a bank and become the most dominant contributor to the bank income. However, the activity to distribute the credit takes a risk which can influence health and continuance of bank business. The credit risk which potentially occurs can be measured and controlled by analyzing directly whichever the credit client categorized to. The credit risk categorized to current credit, in specific concern credit, less current credit, doubtful credit and bad credit based on Bank Indonesia Regulation No.: 7/2/PBI/2005. The independent variables used in this research are nominal credit, principal balance, in time being bank client, time period, and bank interest. Fisher multiple discriminant analysis is a method whose assumption equality of covariance matrices. The result from using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati shows that variable principal balance, in time being bank client, time period, and bank interest significant to measure credit risk.  The classification using the Fisher multiple discriminant analysis in data of credit client from bank “X” in Pati gives the accurate 64,33%. Keywords: credit, classification, fisher multiple discriminant analysis

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Journal Info

Abbrev

gaussian

Publisher

Subject

Other

Description

Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM ...