Debtors Prospective Assessment Application using Naive Bayes at Mitra Sejahtera Cooperative - Indra Griha Tofik Isa; Beni Junedi (The IJICS (International Journal of Informatics and Computer Science), 2022)
Utilization of historical data into new knowledge can increase added value for its users, including Mitra Setia Cooperative (KMS) which has debtor data that is not utilized. “Not Paid Off” potentioal of debtors cannot be detected as early as possible. In this study using the Naive Bayes algorithm in classifying the feasibility of prospective debtors based on the classification of "Paid Off" and "Not Paid Off" based on parameter of Age, Sex, Amount of Loan, Occupation, Income, and Repayment Period. The research stages consist of (1) Research Initiation, (2) Data Selection, (3) Data Preprocessing, (4) System Design, (5) Program Implementation and (6) Program Testing. The purpose of this study is to minimize the increase in bad loans by implementing the Naive Bayes method in the application of the assessment of prospective debtors. The final result is a debtors prospective assessment application at Mitra Sejahtera Cooperative with an accuracy rate of 86%