Aplikasi Multi Platform Penerapan Algoritma Naive Bayes untuk Klasifikasi Keterlambatan Pembayaran Premi Asuransi - Jorgi Antonius Karlia; Wawan Nurmansyah (STRING (Satuan Tulisan Riset dan Inovasi Teknologi), 2022)
Human carelessness can be one of the main factors in accidents. Knowing this situation, the insurance company takes the role as well as the opportunity from the consumers, to be the one who will bear the loss known as risk. The problem that often arises in insurance companies is the number of customers who are in arrears in paying premiums. In the procedures applicable to insurance, there is a grace period for payment of 30 days during which the customer/insured must pay a predetermined amount of premium and if the customer/insured does not pay the premium, the insurance policy will be canceled so that the insurance profit will be reduced and if it happens, it will be detrimental to the insurance. This research is conducted by applying the Naive Bayes algorithm using insurance customer data. The result of this study is a classification system for late payment of insurance premiums that can classify the status of premium payments for insurance customers. The system test results show that the system can classify the premium payment status of insurance customers with an accuracy rate of 82.5%, then the resulting precision level is 94.83% and the resulting recall is 86.39%.