Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika - Oman Somantri; Mohammad Khambali (Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada, 2017)
Classification of short stories category based on age of the reader is still difficult. Therefore, a decision support system to classify the short stories category is needed. Naïve Bayes is one of methods suitable for short stories classification. However, Naïve Bayes has flaws in accuracy level, and needs to be optimized. In this paper, Genetic algorithm is proposed to increase the level of accuracy. In this case, genetic algorithm is used for feature selection. The results show an increase in the level of accuracy produced. The accuracy increases from 78,59% to 84,29%. In conclusion, the application of genetic algorithm on Naïve Bayes in classifying the online short stories category can improve the accuracy.