ANALISIS PENGARUH USIA, JUMLAH PINJAMAN, PENGALAMAN USAHA DAN OMZET USAHA TERHADAP KELANCARAN ANGSURAN PEMBIAYAAN ULTRA MIKRO (UMi)
Keywords:Ultra Micro, UMi, Logistics Regression, Logit, prediction, probability, default
The purpose of this research is to develop a predictive model for the probability of default from Ultra Micro financing customers (UMi) which was initiated by the Government Investment Agency of the Ministry of Finance of the Republic of Indonesia in 2017. The analytical method uses Logistics Regression (Logit) as binary logit by sampling observational data. in 2021 by purposive sampling as many as 398 debtors consisting of 46 defaulted debtors and 352 current debtors. The dependent variable uses binary data with dummy "0" for defaulted debtors, while the dummy data "1" for current debtors and the Independent variable uses a gross revenue, education level, financing plafond provided, business experience and age of the debtor. This study reveals that only the education level and age of the debtor have a significant effect on the probability of non-performing loans to ultra micro customers.