Kurnia Dwi Jayanti
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ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI NON PERFORMING LOAN (Studi pada Bank Umum Konvensional yang Go Public di Indonesia periode 2008-2012) Dwi Jayanti, Kurnia; Haryanto, A. Mulyo
Diponegoro Journal of Management Volume 2, Nomor 3, Tahun 2013
Publisher : Faculty of Economics and Business Diponegoro University

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Abstract

Conventional commercial bank is vulnerable hit by Non Performing Loans (NPL) becausecredit as the main source of income comes from a conventional bank and the risk that might occurshould be handled without involving the customer. Although the bank’s management had madeefforts based on the credit rating of 5C+1C but still the banks potentially exposed tho tb he creditrisk. The purpose of this research was to know how big the influence of variable CAR, LDR, SIZE,KAP and BOPO, against Non Performing Loan (NPL) in a banking company that listed on BEI.The population in this research are 121 banks in Indonesia period 2008-2012. Thesampling technique used was purposive sampling on criteria: (1) conventional commercial bankslisted on BEI period 2008-2012, and (2) conventional commercial banks in their financial reportscontained the required data in the research period 2008-2012. The data is obtained from annualreport of each bank period 2008-2012. This sample gained amount of 23 banks from 121 bankspublic listed in Indonesia. Analytical techniques used was multiple linear regression andhypothesis test using t-statisctic to examine partial regression with level of significance 0,05.Before tested with a multiple linear regression test, testified with classical assumptions test first.The results showed that there were no deviations from the classical assumption. Thosethings indicate the data which avaible in this study has been qualified for use in linear regressionmodel. From the analysis shows that in partially CAR haven’t significant negative effect on NPLand LDR haven’t significant positive affect on NPL, while SIZE, KAP and BOPO have positivelyand significantly effect on NPL. The result of regression estimation show the ability of modelprediction is 35% while the remaining 65% influenced by other factors outside the model that hasnot been includeed in the study.