Application of the Naïve Bayes Algorithm for Student Graduation Analysis

Autor: Mohammad Roesli, Yanna Ika Pratiwi, Rosanita Tritias Utami, Naidah Naing, Erick Akhmad Fahmi Alfa’izy, Yeni Ika Pratiwi, Lely Ana Ferawati Ekaningsih, Khairil Anam, Nur Anim Jauhariyah, Ahmad Munib Syafa’at
Rok vydání: 2018
Předmět:
Zdroj: International Journal of Engineering & Technology. 7:421
ISSN: 2227-524X
DOI: 10.14419/ijet.v7i4.15.23596
Popis: Design an analysis system to find out graduation by comparing previous data and existing data to overcome errors in a college system. By taking data records that are already available to be processed using the naïve Bayes algorithm. This research was conducted at Universitas Maarif Hasyim Latif. In this case, the object of research is to analyze the data of students with naïve Bayes algorithms to find out their graduation. For sampling the data taken is the previous Faculty of Law Student data to be used as training data, to retrieve the entire data using data records that are already available in the Directorate of Information Systems. That the naïve Bayes algorithm can be used in the classification of data in the form of a string or textual. This is based on researchers' trials in taking examples of calculations that have been done before. To compare the results of the classification of graduation analysis using the naïve Bayes algorithm testing is done with a sample of data in the form of training data compared to data testing. From the calculations that have been made, the accuracy is 77.78%.
Databáze: OpenAIRE