Open AccessApplication of Data Mining with Classification Methods for Promotion of New Student Admissions at Muhammadiyah University of Sidoarjo Using Web-Based Naïve Bayes Algorithm
Author(s) -
Vianti Widyasari,
Arief Senja Fitrani
Publication year - 2021
Publication title -
procedia of engineering and life science
Language(s) - English
Resource type - Journals
ISSN - 2807-2243
DOI - 10.21070/pels.v1i2.1062
Subject(s) - naive bayes classifier , promotion (chess) , computer science , algorithm , bayes' theorem , web application , machine learning , data mining , artificial intelligence , world wide web , support vector machine , bayesian probability , politics , political science , law
The University of Muhammadiyah Sidoarjo (UMSIDA) is one of Indonesia's superior and innovative private colleges in developing IPTEKS based on Islamic values for community welfare. UMSIDA that has stood long enough with the number of students received in each year is quite a lot. Each new school year opening, this private college regularly organizes new student admissions (PMB) activities. Admission for new students (PMB) at UMSIDA can be done at pmb.umsida.ac.id.
Therefore, research aims to create data mining applications classification method with the algorithm Naïve Bayes. This research uses the classification method used to Megukur accuracy level. To predict the promotion of new students receiving Muhammadiyah Sidoarjo University (UMSIDA) can be done using the Naïve Bayes algorithm with 7 predefined variables. Offline and online predictor of the dataset of 2601 data is divided into 2 as many as 70% of 2000 Training data and as much as 30% from 601 of Testing data.