A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students - Nurhachita Nurhachita; Edi Surya Negara (Institute of Advanced Engineering and Science, 2021)
The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. So that there is an accumulation of student data continuously. The purpose of this study is to compare deep learning, naïve bayes, and random forest on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is knowledge discovery in database (KDD). The tools used are rapid miner. The attributes used are student ID number, name, program study, faculty, gender, place of birth, date of birth, year of entry, school origin, national examination, type of payment, and nominal payment. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used deep learning bayes results resulted in an accuracy value of 52.65%, naïve bayes results resulted in an accuracy value of 99.79%, and random forest results resulted in an accuracy value of 44.65%.