KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL DAN NAÏVE BAYES (Studi Kasus pada Asuransi AJB Bumiputera Tanjung Karang Lampung) - Ria Sutitis; Suparti Suparti; Dwi Ispriyanti (Jurnal Gaussian, 2015)
In the insurance companies a problem that often arises is the amount of customer debt in paying premiums, so it needs a system that can classify customers in the group not well, less smoothly, and smooth in paying premiums. Used two methods to perform the classification of payment premium status which is Regression Logistics Ordinal and Naïve Bayes. Variables used in determining whether a payment premium status are gender, marital status, age, work, income, insurance period, and the payment of premium. In Regression Logistics Ordinal, significant variables to the model are gender, marital status, age, insurance period, and the payment of premium. For significant variables used in the classification. Payment premium status of the data processing methods of Regression Logistics Ordinal with accuracy obtained is equal to 50.90% and the Naïve Bayes method obtained is equal to 55.41%. Based on the level of accuracy, the classification of data payment premium status of insurance AJB Bumiputera Tanjung Karang Lampung using the Naïve Bayes method has a greater degree of accuracy than the Regression Logistics Ordinal method. Keywords: Payment Premium Status, Classification, Naïve Bayes, Regression Logistics Ordinal