Analisa Faktor yang Mempengaruhi Pemberian Kredit Kendaraan Bermotor pada Debitur PT.Cakrawala Citra Mega Multifinance Pematangsiantar - Rosintan Sipayung; Fandra Dikhi Januardani; Jesika Melina Simamora (Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai, 2025)
This study examines the dominant factors influencing motor vehicle loan decisions among borrowers at PT. Cakrawala Citra Mega Multifinance in Pematangsiantar. Employing a deductive-survey design, data were collected from 31 randomly selected debtors who obtained motor vehicle financing between June and November 2011. Primary data were gathered via structured questionnaires and analyzed using validity and reliability tests, multiple linear regression, t-tests, F-test, and coefficient of determination (R²) analysis. The regression model yielded the following equation: Y=−0.741+0.558X1+0.241X2+0.517X3 where Y represents loan approval decision, X₁ income, X₂ character, and X₃ collateral. Statistical tests reveal that income (t = 2.764, p < 0.05), character (t = 2.164, p < 0.05), and collateral (t = 2.612, p < 0.05) each exert a significant positive effect on credit decisions. The F-test (F = 13.969, p < 0.01) confirms the joint significance of these variables, while an R² of 0.608 indicates that 60.8% of the variance in credit decisions is explained by the three factors. These findings substantiate the applicability of the 5C framework particularly capacity, character, and collateral in multifinance credit assessment. The study contributes empirical evidence supporting the prioritization of borrower income capacity as the strongest predictor of financing approval, followed by collateral value and borrower character assessment. Practical implications suggest that multifinance institutions should calibrate credit-scoring models to weight income more heavily while maintaining rigorous collateral evaluation and character appraisal. Future research is recommended to integrate macroeconomic indicators and advanced credit-scoring technologies to enhance predictive accuracy and risk mitigation in the Indonesian motor vehicle financing sector.