Perbandingan Algoritma SVM, Random Forest Dan XGBoost Untuk Penentuan Persetujuan Pengajuan Kredit

Credit is an option for seeking funding most economic activities. The demand credit currently growing very rapidly, in line with the increasing financial needs of community, especially developing countries such as Indonesia. analysis to be carried out achieve proper and safe lending. observation see feasibility a problem. From this analysis, creditworthiness recipient will known. This study uses CRISP-DM methodology which consists 6 stages, namely Bussines Understanding, Data preparation, Modeling Evaluation, Deployment by applying classification method comparing SVM, Random Forest, XGBoost algorithms. research open source dataset obtained from Kaggle. results using random forest, algorithms get highest accuracy, recall, precision values model 82% 70% 92% precision.