Klasifikasi Dokumen SAMBAT Online Menggunakan Metode Naive Bayes dan Seleksi Fitur Berbasis Algoritme Genetika - Tony Faqih Prayogi; Imam Cholissodin; Edy Santoso (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Integrated Community Asking Application System (SAMBAT) Online is one of application that becomes an eGov system in Malang City to provide a place for the people of Malang City to voice their aspirations towards problems that exist for the good of the city itself. All complaints that enter through SAMBAT Online have been grouped based on the existing parts and later will be sorted manually and forwarded to the respective Regional Work Unit (SKPD) so that they can be immediately followed up. But because of the number of complaints received so long enough to be processed by each SKPD. Therefore a system was created for the classification of SAMBAT Online documents. In this study implemented a naive bayes method and genetic algorithm-based feature selection for the SAMBAT Online document classification. The implementation process itself consists of preprocessing, term weighting, Feature Selection using genetic algorithms and the classification process using naive bayes method. The results of the tests that have been done, obtained the highest accuracy of 89.79% in the test of 49 data test with the parameter value of generations 70, population size 20, crossover rate 0.8 and mutation rate 0.2.