Mapping Top Grade Students Using the K-Means Clustering Algorithm

  • Juniar Hutagalung STMIK TRIGUNA DHARMA


 The purpose of this study was to classify the data of superior class students so as to facilitate student education based on differences inability to learn and participate in learning at school. Grouping students with more and more data becomes less effective and efficient. Moreover, grouping students who have achievements to enter the superior class, of course, requires a computer-based system. Every year the number of students increases, thus the number of student data also continues to increase and there is a process of data accumulation that is difficult to process optimally. Conventional decision-making, cannot handle very large amounts of data. For this reason, data mining is needed in order to solve these problems through patterns of data accumulation. The application of k-means with a sample of 120 data from student report cards of SMK Raksana 2 Medan, can accelerate the grouping of superior class students by inputting the centroid value and finding the closest value so as to produce 3 clusters, namely: cluster 1 mastery of android programming as many as 10 students, cluster 2 mastery web programming as many as 62 students and cluster 3 mastering desktop programming as many as 48 students. The application of data mining with a web-based k-means algorithm to process it into information and knowledge as consideration for educators in the decision-making process.


[1] M. L. Sibuea and A. Safta, “Pemetaan Siswa Berprestasi Menggunakan Metode K-Means Clustring,” Jurteksi, vol. 4, no. 1, pp. 85–92, 2017, doi: 10.33330/jurteksi.v4i1.28.
[2] M. Clustering, S. Kasus, M. Arrohman, and K. Langkap, “PENGELOMPOKAN KARAKTER SISWA DENGAN MENGGUNAKAN METODE K- Proses dasar algoritma K-Means ( Wardhani , 2016 ) : 1 . Tentukan k sebagai jumlah cluster yang ingin dibentuk . Tetapkan pusat cluster . 2 . Hitung jarak setiap data ke pusat cluster menggunakan p,” no. 1510651003.
[3] M. Kelas and U. Pada, “IMPLEMENTASI ALGORITMA K-MEANS UNTUK,” vol. 1, no. 1, pp. 128–135, 2020.
[4] M. Mardalius, “Implementasi Algoritma K-Means Clustering Untuk Menentukan Kelas Kelompok Bimbingan Belajar Tambahan (Studi Kasus : Siswa Sma Negeri 1 Ranah Pesisir),” 2018, doi: 10.31219/
[5] A. Sulistiyawati and E. Supriyanto, “Implementasi Algoritma K-means Clustring dalam Penetuan Siswa Kelas Unggulan,” vol. 15, no. 2, pp. 25–36, 2020.
[6] K. R. Nirmal and K. V. V. Satyanarayana, “Issues of K means clustering while migrating to map reduce paradigm with big data: A survey,” Int. J. Electr. Comput. Eng., vol. 6, no. 6, pp. 3047–3051, 2016, doi: 10.11591/ijece.v6i6.11207.
[7] T. Jaringan, J. Hutagalung, and U. F. Sari, “InfoTekJar : Jurnal Nasional Informatika dan Penerapan Metode K-Means dan MOORA Dalam Penerimaan Bantuan Stimulan Perumahan Swadaya ( BSPS ),” vol. 1, 2021.
[8] J. Hutagalung, N. L. W. S. R. Ginantra, G. W. Bhawika, W. G. S. Parwita, A. Wanto, and P. D. Panjaitan, “COVID-19 Cases and Deaths in Southeast Asia Clustering using K-Means Algorithm,” J. Phys. Conf. Ser., vol. 1783, no. 1, 2021, doi: 10.1088/1742-6596/1783/1/012027.
[9] T. R. Stella Mary and S. Sebastian, “Predicting heart ailment in patients with varying number of features using data mining techniques,” Int. J. Electr. Comput. Eng., vol. 9, no. 4, pp. 2675–2681, 2019, doi: 10.11591/ijece.v9i4.pp2675-2681.
[10] G. Bathla, H. Aggarwal, and R. Rani, “A novel approach for clustering big data based on mapreduce,” Int. J. Electr. Comput. Eng., vol. 8, no. 3, pp. 1711–1719, 2018, doi: 10.11591/ijece.v8i3.pp1711-1719.
[11] M. Z. Hossain, M. N. Akhtar, R. B. Ahmad, and M. Rahman, “A dynamic K-means clustering for data mining,” Indones. J. Electr. Eng. Comput. Sci., vol. 13, no. 2, pp. 521–526, 2019, doi: 10.11591/ijeecs.v13.i2.pp521-526.
[12] D. D. Darmansah, “Analisis Penyebaran Penularan Virus Covid-19 di Provinsi Jawa Barat Menggunakan Algoritma K-Means Clustering,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 3, pp. 1188–1199, 2021, doi: 10.35957/jatisi.v8i3.1034.
[13] J. Hutagalung and F. Sonata, “Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah Asuransi,” vol. 5, pp. 1187–1194, 2021, doi: 10.30865/mib.v5i3.3113.
[14] H. Xie et al., “Improving K-means clustering with enhanced Firefly Algorithms,” Appl. Soft Comput. J., vol. 84, p. 105763, 2019, doi: 10.1016/j.asoc.2019.105763.
[15] T. Jyothirmayi, K. S. Rao, P. S. Rao, and C. Satyanarayana, “Image segmentation based on doubly truncated generalized laplace mixture model and K means clustering,” Int. J. Electr. Comput. Eng., vol. 6, no. 5, pp. 2188–2196, 2016, doi: 10.11591/ijece.v6i5.10682.
[16] A. M. A. Alan Fuad Jahwar, “Meta-Heuristic Algorithms For K-Means Clustering : A Review,” Pjaee, vol. 17, no. 7, pp. 1–20, 2021.
[17] J. Albert-Smet, A. Torrente, and J. Romo, “Band Depth based initialization of $k$-Means for functional data clustering,” 2021, [Online]. Available:
[18] W. L. Al-Yaseen, A. Jehad, Q. A. Abed, and A. K. Idrees, “The Use of Modified K-Means Algorithm to Enhance the Performance of Support Vector Machine in Classifying Breast Cancer,” Int. J. Intell. Eng. Syst., vol. 14, no. 2, p. 190, 2021, doi: 10.22266/ijies2021.0430.17.
[19] A. E. Pramitasari and Y. Nataliani, “Perbandingan Clustering Karyawan Berdasarkan Nilai Kinerja Dengan Algoritma K-Means Dan Fuzzy C-Means,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 3, pp. 1119–1132, 2021, doi: 10.35957/jatisi.v8i3.957.