Open AccessPENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)
Author(s) -
Komang Aditya Pratama,
Gede Aditra Pradnyana,
I Ketut Resika Arthana
Publication year - 2020
Publication title -
sintech (science and information technology) journal
Language(s) - English
Resource type - Journals
eISSN - 2598-9642
pISSN - 2598-7305
DOI - 10.31598/sintechjournal.v3i1.523
Subject(s) - test (biology) , naive bayes classifier , computer science , bayes' theorem , entrance exam , state (computer science) , process (computing) , mathematics education , artificial intelligence , psychology , operating system , curriculum , bayesian probability , pedagogy , algorithm , support vector machine , paleontology , biology
Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)”. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.