IMPLEMENTASI TEORI NAIVE BAYES DALAM KLASIFIKASI CALON MAHASISWA BARU STMIK KHARISMA MAKASSAR - Irayori Loelianto; Moh. Sofyan S Thayf; Husni Angriani (SINTECH (Science and Information Technology) Journal, 2020)
STMIK KHARISMA Makassar has graduated thousands of alumni since it was founded. However, the number of students registering is uncertain every year, although from 2016 to 2019 there has been an increase in the number of registrations. The problem is the percentage of the number of prospective students registering has actually decreased significantly. The purpose of this research is to implement the Naive Bayes theory in classification of STMIK KHARISMA Makassar prospective students. This research basically uses the Naive Bayes theory as a classifier, and is made using the Python programming language. At the classifier design stage, there were a total of 499 data collected from 2016 to 2019. The data was divided by a ratio of 80:20 for training data and test data. The result from the research indicate the level of accuracy of the classifier reaches 73%.