K-Nearest Neighbor and Random Forest Algorithms in Loan Approval Prediction

Abstrak

Loan approval prediction is an important task in the financial sector, which helps banking institutions and lenders make informed decisions regarding loan applications. This research compares the performance of two machine learning algorithms, namely K-Nearest Neighbor (KNN) and Random Forest (RF), in the context of loan approval prediction. The research methodology includes data collection, pre-processing, modeling, and evaluation. The analysis results showed that the Random Forest model performed better overall than KNN, with more true positives and true negatives, and fewer false positives and false negatives. In addition, Random Forest recorded higher accuracy, precision, recall, and F1-score values. These findings provide valuable insights for financial institutions in improving credit risk management strategies and decision-making regarding loan applications.