IMPLEMENTASI ALGORITMA RANDOM FOREST TERHADAP PREDIKSI GOOD LOAN/BAD LOAN KREDIT NASABAH BANK DI JAKARTA - Sultan Farel Syah Reza (2023)
Credit is the granting (guarantee) of goods or services by one party with money to meet all the needs, wants and aspirations of society, based on increasingly competitive society. Credit risk is the risk of loss associated with the inability and/or unwillingness of the borrower to fulfill its obligation to repay the loan in full on or after the maturity date. In granting credit, banks must identify, manage and ensure credit risk in all products and must go through an appropriate risk management control process. Therefore, a system is needed which is able to predict the credit risk posed by bank customers who are unable to pay off credit loans so that the bank does not lose money. Using data obtained from ID/X to created a machine learning model using the Random Forest algorithm. The output generated from the model that has been made is the classification of bank customers into good loans / bad loans. The classification model obtained will be evaluated using accuracy, precision, recall, and F1-Score values. The best evaluation results are obtained with a model ratio of 70% training data and 30% testing data, achieving an accuracy of 84.32%, precision of 96.79%, recall of 86.44%, and F1-score of 91.3%.