Pemodelan Prediksi Predikat Kelulusan Mahasiswa Menggunakan Fuzzy C-Means Berbasis Particle Swarm Optimization - Hardi Jamhur (Sekolah Tinggi Ilmu Komputer Binaniaga, 2020)
Analysis and excavation of information on educational data is an inseparable part of organizing student academic systems in universities. The excavation of information is known as Educational Data mining (EDM), which is a discipline that focuses on the application of techniques and data mining devices specifically in the field of education. One of the EDM processes is related to the need for prediction especially to gain knowledge regarding the progress of student studies. Practically the activity of getting information / knowledge about the progress of student studies is difficult to do conventionally considering the size of the data volume is quite large. The approach to data mining clustering on student study progress data for prediction of predicate graduation tends to be not optimal. Fuzzy C-means (FCM) modeling optimized with Particle Swarm Optimization (PSO) can produce a more optimal prediction performance. In the case of prediction of student graduation prediction, the application of PSO-based FCM algorithm model produces more optimal results, with predictive accuracy of 86% while the FCM algorithm modeling is 79%.