Perbandingan Metode Klasifikasi Naïve Bayes Dan Jaringan Saraf Tiruan: Studi Kasus: Pt Asuransi Jiwa Bersama Bumiputera Tahun 2018 - Ardyanti, Hesti; Goejantoro, Rito; Amijaya, Fidia Deny Tisna (Program Studi Statistika FMIPA Universitas Mulawarman, 2020)
Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new object. Naïve Bayes is a classification technique for predicting future probability based on past experiences with a strong assumption of independence. Artificial neural network is one of the data mining analysis tools that can be used to create data on classification. Model selection in artifial neural networks requires various factors such as the selection of optimal number of hidden neuron. This research has a goal to compare the level of classification accuracy between the Naïve Bayes method and artificial neural network on payment status of the insurance premium. The data used is insurance costumer’s data of PT AJB Bumiputera Samarinda in 2018. The result of the comparison of accuracy calculation from the two analyzes indicate that artificial neural network has a higher level of accuracy than naïve Bayes method. Classification accuracy result of Naïve Bayes is 82,76% and artificial neural network is 86,21%.