Penerapan Algoritma CLARANS Data Mining untuk Klasterisasi Nilai Mahasiswa Pada Penentuan Bidang Konsentrasi - Harmanda, Inke; Sari, Anggun Puspita; Melasari, Melasari; Angkat, Erlita Natasya; Sembiring, David JM; Ramles, Polin (Journal Global Technology Computer, 2025)
A major challenge for educational institutions is recognizing their students' academic abilities and guiding them toward the right concentration. Grouping concentration areas for students is not easy. Grouping concentration areas will help students focus more on a concentration they are interested in and align it with their academic grades. The urgency of this research lies in the need to present a more objective, accurate, and data-driven method for grouping student concentration areas. With a recommendation system supported by data mining techniques, the process of determining concentration areas depends not only on students' personal preferences but also considers relevant academic performance patterns. This problem can be solved by utilizing data mining techniques, specifically the clustering method using the CLARANS algorithm. This study aims to analyze student data according to the weighting of certain course grades using the Clarans Algorithm, thus being able to provide decision support for grouping student grades to determine which major a student should be enrolled in. Student grade data with high (Network), medium (Programming), and low (Internet of Things) grades can be grouped into three clusters. The test results showed that 11 students were enrolled in the programming concentration, 5 students in the networking concentration, and 9 students in the Internet of Things concentration.