| dc.description.abstract | Bank Sumut mortgage debtors often face fluctuations in collectibility status influenced
by various economic and personal factors. To overcome this problem, the XGBoost
method is used because of its effective ability to handle classification problems and
provide accurate predictions. This study aims to develop a prediction system that can
help Bank Sumut in improving the efficiency of credit decision making and risk
management. In this study, the XGBoost model was built and trained with data covering
factors such as ceiling, ending balance, interest, principal, and product name. The
results showed that this model successfully predicted the debtor's collectibility status
with a very good accuracy rate of 97.31% using 80% training data and 20% test data.
With an accurate prediction system, Bank Sumut can be more effective in managing its
credit portfolio and making more appropriate credit decisions. This research is
expected to contribute to the advancement of science and technology in the banking
sector, especially in credit analysis and risk management. | en_US |
