Optimasi Algoritma Naïve Bayes Menggunakan Algoritma Genetika Untuk Memprediksi Kelulusan: Studi Kasus: Mahasiswa Jurusan Matematika FMIPA Universitas Mulawarman - Feronica, Elisa; Nasution, Yuki Novia; Purnamasari, Ika (EKSPONENSIAL, 2022)
The Naïve Bayes algorithm is classification method that uses the principle of probability to create predictive models. Naïve Bayes is based on the assumption that all its attributes are independent which can be optimized by genetic algorithms. Genetic algorithm is an optimization technique which works by imitating the process of evaluating and changing the genetic structure of living creatures. In this study, the Naive Bayes algorithm was optimized using by genetic algorithm to predict student graduation with attributes, namely gender, regional origin, admission path and employment status. The data used is the students of the Mathematics Department, Faculty of Mathematics and Natural Sciences, Mulawarman University who graduated in March 2018 to December 2020. The results of this study indicate the accuracy value generated by Naïve Bayes of 50% increased by 16,67% after the attributes were optimized by using the genetic algorithm to 66,67% with 3 selected attributes, namely regional origin, admission path and employment status