PENGELOMPOKKAN PERFORMA AKADEMIK MAHASISWA BERDASARKAN INDEKS PRESTASI MENGGUNAKAN K-MEANS CLUSTERING

Rachmad Zaini Alberto, Winda Kurnia Sari, Samsuryadi -, Anggina Primanita

Abstract


Abstract— Indeks Prestasi (IP) or Grade Point Average (GPA)
is a means that can be used as student performance indicator.
Understanding student performance is expected to help academic
institution to decide which treatment to be given to students. In
this paper, K-Means Clustering algorithm is used to group
university students academic performance into 4 clusters based
on students’ first four semesters IP. Each clusters has random
initial centroid. Centroid is checked in every iteration, until there
is no more change. Based on 50 experiments in grouping 100
students academic performance data, the average error rate is
0.92%. This shows that K-Means clustering is able to group
students academic performance consistently

Intisari—Performa akademik mahasiswa berdasarkan
perolehan nilai indeks prestasi (IP) digunakan sebagai
indikator untuk menyelesaikan masa perkuliahan mahasiswa.
Pada penelitian ini algoritma K-Means Clustering digunakan
untuk mengelompokkan performa akademik mahasiwa
menjadi 4 cluster. Setiap cluster memiliki centroid awal yang
ditentukan secara acak. Centroid mengalami perubahan jika
setiap proses clustering ada perpindahan data pada tiap cluster
dan perubahan centroid berhenti apabila tidak ada lagi
perubahan data. Berdasarkan 50 kali percobaan untuk
mengelompokkan 100 data, akurasi pengelompokkan performa
akademik berdasarkan nilai rata rata standard error adalah
sebesar 0,92%. Hasil ini menunjukkan pengelompokkan
performa akademik yang konsisten

Kata Kunci— K-Means Clustering, Grade Point Average, Indeks
Prestasi


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A-104


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