Aplikasi Data Mining Dengan Algoritma Naive Bayes Untuk Memprediksi Tingkat Kelulusan Mahasiswa (original) (raw)

Stacks of data from company or agency transactional systems are assets that are currently not utilized for analysis to produce new knowledge or information that is very valuable in the future. The purpose of this study is to generate new knowledge about graduation rates, by utilizing student graduation data in 5 years, which can be used to determine whether students graduate on time or not. The Data Mining method used is Naïve Bayes which is a simple probability classification method that calculates probabilities by adding up the frequency and combination of values from existing data sets. The research method used is RUP (Rational Unified Process) with stages including: Inception, Elaboration, Construction, and Transition. The data used is the graduation data of students who graduated from 2016-2019 at XYZ college. The attributes used are, Gender, Age, Origin, Class, Major, GPA. As a preventive measure by using a web application, which can be accessed anywhere and anytime by students, the prediction results can motivate students in their completion, make it easier for the campus to predict graduation rates and assist in designing remedial steps to improve campus quality, as well as campus accreditation scores, so that the level of stakeholder trust can increase in the future.

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