<![CDATA[Electronic goods and furniture for some people are currently seen as basic needs that must be met. High prices make it difficult for people to meet their needs with cash purchases, so they choose credit purchases using the services of finance companies in purchasing goods. This study aims to determine the factors that influence the status of credit payments for electronic goods and furniture at PT. KB Finansia Multi Finance Bontang 2020 uses logistic regression. Based on the results of the analysis, it was found that the predictor variables that had a significant effect on the credit payment status response variable were length of stay (domicile) at the address borne by the debtor when applying for credit (X3) and the amount of credit payments charged by the debtor per month (X6). The value of the Apparent Error Rate (APER) of 29.323% indicates that the logistic regression model obtained is also good for solving cases of current and non-current classification of credit payment status.]]>
