KLASIFIKASI PESAN SMS MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN SELEKSI FITUR GENETIC ALGORITHM - Indah Munitasri; Stefanus Santosa; Catur Supriyanto (Jurnal Teknologi Informasi - Cyberku (JTIC), 2019)
Short Message service (SMS)is mobile communication that interest advertiser for its effective deliveries with cheap operational cost compare to printed media. Some spam SMS do not need mailing list to reach their customers. But, spam SMS could create higher respons from emails spam. Spam SMS includes promotion,scamming,and fraud.To overcome this problem,anti-spam filtering are needed to detect spam and non-spam SMS. Some anti-spam filtering algoritm such as Decission Tree, Naïve Bayes (NB),Support Vector Machine (SVM),and Neural Network. This research used Naïve Bayes classifier or known as multinominal Naïve Bayes is a simplification from Bayes algoritm which is suitable for text or documents classification.This study will make additional Genetic Algorithms in the process of selecting attributes that will be used in the classification process with Naïve Bayes algorithm. Genetic Algorithms can be used as an attribute of the overall voter attributes obtained from the process of feature extraction. NB compared to NB and GA produced significant accuracy result, NB gained 89.39% accuracy rate, but GA gained 89.73% accuracy rate. So, there is an increase in 0.34 % after adding GA. NB and GA can be applied to the classification of SMS messages, because Naïve Bayes algorithm is an algorithm that does not consider the relationship between attributes to one another (independence). So, when there is a data set with hundreds of attributes, all of those attributes will be counted by Naïve Bayes, by adding a Genetic Algorithm as a feature selection, which determines the attributes that are relevant in order to optimize the classification accuracy. It is expected to apply feature selection using Particle Swarm Optimization (PSO) for the next research because there is no evolution in the operator, for example, mutation and crossover on Genetic Algorithms (GA,) and PSO is more flexible in maintaining the balance between global and local searches on its search space.