(untitled) (Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI), 2024)
Abstract This journal examines the use of classification algorithms such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM) in providing loans to customers. This method is used to increase the reliability and accuracy of the credit risk evaluation system. The experimental methodology involves a dataset containing variables related to credit history, income, and other risk factors. The research results show that the KNN algorithm achieves a significant level of accuracy in identifying customer risk profiles. On the other hand, Naive Bayes successfully handles data with dependencies between variables, and SVM provides consistent results in handling complex datasets. This research explores the benefits and drawbacks of each algorithm to help build a better decisionmaking system for customer lending. Keywords: KNN, Naïve Bayes, SVM, Customer Loans.