IMPLEMENTASI CORRELATION BASED FEATURE SELECTION (CFS) UNTUK PENINGKATAN AKURASI ALGORITMA C4.5 DALAM PREDIKSI PERFORMA AKADEMIK MAHASISWA BERBASIS LEARNING MANAGEMENT SYSTEM - Rizky Aspiah; Taghfirul Azhima Yoga Siswa (LPPM Sekolah Tinggi Teknologi Pagar Alam, 2022)
The COVID-19 pandemic in the education aspect has an impact on the learning system. Muhammadiyah University of East Kalimantan utilizes the OpenLearning platform as a medium to support online learning. In this study, predictions of the performance of UMKT students in online lectures were carried out using a data mining approach. The dataset used comes from the OpenLearning platform and the Academic Administration Section which contains 2,663 data. This study aims to identify the best attributes, implement the C4.5 algorithm modeling, and search for modeling searches using a confusion matrix. The results showed that the influential indicators included time spent on courses, completed courses, assignments, quizzes, uts and labels. The implementation of the C4.5 algorithm produces a decision tree at the initial node of the quiz variable with a rank of -0.80 which is included in the true group, but if the rank is -0.80 it is included in the false group. The results of the accuracy using the distribution data of 80% training data and 20% testing data are 97.22%.