PREDIKSI TINGKAT KELANCARAN PEMBAYARAN KREDIT BANK DENGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN K-NEAREST NEIGHBOR

Farida Gultom (STMIK Mikroskil)
Tober Simanjuntak (Universitas Efarina)

METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Vol. 4 No. 2 (2020): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi

Application developers and users are the main keys to the market's impact on application development. In developing applications, it is necessary to predict applications in the market accurately, accurate prediction results are very important in showing the correct rating and decision making in the selection of new prospective debtors. Tests conducted in this test use a dataset of credit customers from Bank Danamon. This study predicts the smooth payment of credit by combining the Naïve Bayes and K-Nearest Neighbor methods. Predicting the smooth level of credit payments is done by combining the Naïve Bayes algorithm and K-Nearest Neighbor in order to predict the smooth payment of credit in the future, this can be seen from the prediction results obtained by 80%.