PREDIKSI TINGKAT KELULUSAN MAHASISWA PROGRAM STUDI SISTEM INFORMASI DI UNIVERSITAS XXX MENGGUNAKAN ALGORITMA NAÃVE BAYES - Mega Wahyuningsih; Hari Purwanto; Muryan Awaludin (Fakultas Ilmu Komputer dan Desain - Unsurya, 2024)
The Information System Study Program at UNIVERSITAS XXX is a new major and the graduation of students is still few. Based on data obtained from graduates of the 2018/2019 academic year, 41 students graduated, including 26 students who experienced delays in taking their studies. The need for a system that can predict student graduation in order to major in Information Systems can produce more student graduations than ever before. Data mining using the NaÃ¯ve Bayes algorithm method is one of the methods that utilizes probability calculations and statistics to predict future probabilities based on previous experience. NaÃ¯ve Bayes is able to be applied in predicting graduation by utilizing previous student graduation data, The attributes used are Gender, Age, SKS, GPA, and Student Status. The results of research testing using RapidMiner 9.8 with 100 training data and 60 testing data, result accuracy 90%, recall 93.94%, and precision 88.57%. A computerized system built on a web basis using the PHP programming language.Â Keywords: Graduation, Data Mining, NaÃ¯ve Bayes, and Rapid Miner