Credit Card Risk Classification Using K-Nearest Neighbor Weighted Algorithm Based on Forward Selection

Sartika Dewi Purba (STMIK Mikroskil)
Pahala Sirait (STMIK Mikroskil)
Arwin Arwin (STMIK Mikroskil)

Jurnal Mantik
Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)

One form of credit card risk is non-performing credit cards, which describe a situation where loan repayment approval on credit cards runs the risk of failure. In the classification technique there are several algorithms that can be used, one algorithm that is often used is Weighted k-nearest neighbor (WKNN). This study aims to improve the performance of the Weighted k-nearest neighbor (WKNN) algorithm by applying the forward selection feature that is used to select each unused feature when starting a feature iteration, the results of the study show that by adding forward performance selection of the Weighted k-nearest algorithm neighbor (WKNN) get a better value that is 86.4%, compared to using the Weighted k-nearest neighbor (WKNN) algorithm without a forward selection that is equal to 60.1%.