Students Grade Grouping to Optimize On-Time Graduation Predictions by Combining K-Means and C4.5 Algorithms (Case Study: University Potensi Utama) - Bob Subhan Riza; Sarjon Defit (Telematika, 2021)
Graduating on time is the dream of every student who studies in universities. Some factors that can lead to failure in graduating on time, such as grades, though students are sometimes careless and underestimating this factor, despite knowing that problematic Grade will hinder the student from graduating on time. This research helps the study program to predict which students will graduate on time. There are 2 stages in the research, first is the process of clustering students' data using the K-Means algorithm, while the second stage predicts students' graduation using the C4.5 algorithm. Variable used are Grade, Failing Grade, Specialization, Internship, Thesis, Undergraduate Thesis 1, Undergraduate Thesis 2, and Passing Grade. Using RapidMiner and processing these data using this software can predict students that graduate on time.