Penerapan Data Mining Untuk Memprediksi Prestasi Akademik Mahasiswa Menggunakan Algoritma C4.5 dengan CRISP-DM
Abstract
Because student data can be used to examine student academic accomplishment, or student achievement index data, student data is a very valuable database. Data on student performance is available at Muhammadiyah University of Bangka Belitung's Faculty of Engineering and Science, which houses study programs in computer science, civil engineering, and natural resource conservation. The data are analyzed using them. The C4.5 Algorithm is used in conjunction with a classification data mining technique on student data to forecast academic progress. A decision tree is constructed using algorithm C4.5. Decision trees are helpful for investigating data and uncovering undiscovered connections between numerous input factors and one goal variable. Performance outcomes are derived from the analysis results by categorizing student data. This serves as a resource for lecturers and students to enhance classroom learning and discipline among students.
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Copyright (c) 2023 Suprayuandi Pratama, Iswandi Iswandi, Andre Sevtian, Tsabita Putri Anjani
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