Analisis Pengelompokan Data Nilai Siswa untuk Menentukan Siswa Berprestasi Menggunakan Metode Clustering K-Means

Authors

  • Erdi Amos Saputra Satya Wacana Christian University, Indonesia
  • Yessica Nataliani Satya Wacana Christian University, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v3i3.164

Keywords:

Data Mining, Clustering K-Means, Data Murid, Prestasi Murid, SAW, Top Rank

Abstract

Dalam data mining, pendekatan K-Means Clustering adalah metode yang digunakan untuk mengelompokkan data menjadi kumpulan data. Dalam sistem analisis, pendekatan data mining berdasarkan algoritma K-Means dapat digunakan untuk pengelompokan prestasi murid. Dalam penelitian ini data nilai siswa kelas X-XII Bahasa SMAN 1 Tengaran tahun 2014-2017, dari semester satu sampai lima dikelompokkan berdasar nilai rapor. Clustering digunakan dalam pembangunan program analitik ini untuk menilai dampak data murid terhadap kecenderungan keberhasilan murid di setiap kelompok yang dapat dibuktikan dengan kelulusan murid yang menduduki top rank serta dari hasil wawancara guru pengajar maupun wali kelas serta data nilai yang diperoleh dari Dapodik. Hasil dari penelitian ini membuktikan bahwa teknik clustering K-Means dapat dimanfaatkan oleh pengajar untuk mengkategorikan murid berdasarkan nilai mata pelajaran dan absensi, serta menggunakannya untuk menganalisis prestasi murid dengan mengelompokkan dari kategori prestasi rendah, rata-rata, dan tinggi. Selanjutnya, dengan metode Simple Additive Weighting (SAW) dicari top rank dari cluster tinggi untuk menemukan murid unggulan.

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Published

2021-10-21

Issue

Vol. 3 No. 3 (2021): Journal of Information Systems and Informatics

Section

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How to Cite

[1]
E. A. Saputra and Y. Nataliani, “Analisis Pengelompokan Data Nilai Siswa untuk Menentukan Siswa Berprestasi Menggunakan Metode Clustering K-Means”, journalisi, vol. 3, no. 3, pp. 424–439, Oct. 2021, doi: 10.51519/journalisi.v3i3.164.

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