Perbandingan Algoritma K-Means dan Hierarchical Clustering dalam Pengelompokan Prestasi Akademik Siswa
DOI:
https://doi.org/10.22441/format.2026.v15.i1.008Kata Kunci:
Clustering, K-Means, Hierarchical Clustering, Student Performance, Silhouette ScoreAbstrak
Pengelompokan prestasi akademik merupakan salah satu strategi yang dapat membantu guru dan pihak sekolah dalam melakukan intervensi pembelajaran, seperti pemberian bimbingan tambahan atau penentuan strategi pengajaran yang lebih tepat. Penelitian ini bertujuan untuk membandingkan kinerja algoritma K-Means dan Hierarchical Clustering dalam mengelompokkan prestasi akademik siswa. Dataset yang digunakan terdiri dari 1.000 data siswa dengan tiga atribut nilai, yaitu Matematika, Membaca, dan Menulis, yang diperoleh dari sumber data publik. Tahapan penelitian meliputi proses preprocessing, normalisasi data menggunakan StandardScaler, penerapan algoritma clustering, serta visualisasi hasil menggunakan scatter plot dua dimensi dan dendrogram. Evaluasi kinerja model dilakukan menggunakan Silhouette Score untuk menilai kualitas pemisahan cluster. Hasil penelitian menunjukkan bahwa algoritma K-Means memperoleh skor Silhouette sebesar 0,406, sedangkan Hierarchical Clustering memperoleh skor 0,374. Nilai tersebut mengindikasikan bahwa K-Means menghasilkan struktur pengelompokan yang lebih baik dan lebih jelas dalam membedakan tingkat prestasi siswa menjadi tiga kategori: rendah, sedang, dan tinggi. Dengan demikian, K-Means dinilai lebih sesuai untuk analisis pengelompokan prestasi akademik pada dataset ini.
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Referensi
M. Habibullah Arief and M. Kholila Fadhil, “Educational Data Mining for Student Academic Performance Analysis,” 2024. [Online]. Available: https://doi.org/10/25047/jtit.v11i2.5627
R. Awalia, N. Makassar Jl Mallengkeri Raya, K. Makassar, and S. Selatan, “Penerapan Metode K-Means Clustering untuk Pengelompokan Prestasi Siswa menggunakan Orange Data Mining: Studi Kasus di MTs Muhammadiyah Tallo Makassar,” 2020.
V. V. Romanuke, “RANDOM CENTROID INITIALIZATION FOR IMPROVING CENTROID-BASED CLUSTERING,” Decision Making: Applications in Management and Engineering, vol. 6, no. 2, pp. 734–746, 2023, doi: 10.31181/dmame622023742.
N. Bili et al., “Universitas Kristen Wira Wacana Sumba Fakultas Sains dan Teknologi SATI: Sustainable Agricultural Technology Innovation PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK PENGELOMPOKAN PERFORMA SISWA PADA PEMBELAJARAN BAHASA INDONESIA(Studi Kasus: SD INPRES WAINGAPU 3) Aplication Of The K-Means Clustering Algorithm For Grouping Student Performance In Indonesia Language Learning ( Case Study: SD INPRES WAINGAPU 3),” 2024.
E. Palumbo, M. Vandenhirtz, A. Ryser, I. Daunhawer, and J. E. Vogt, “From Logits to Hierarchies: Hierarchical Clustering made Simple,” Oct. 2024, [Online]. Available: http://arxiv.org/abs/2410.07858
M. Sholeh and D. Andayati, “Penerapan Metode Clustering dengan Algoritma K-Means Pada Pengelompokan Indeks Prestasi Akademik Mahasiswa,” SKANIKA: Sistem Komputer dan Teknik Informatika, vol. 6, no. 1, pp. 51–60, 2023.
K. Aoyama and K. Saito, “Accelerating spherical K-means clustering for large-scale sparse document data,” Nov. 2024, [Online]. Available: http://arxiv.org/abs/2411.11300
H. Jowhari, “Hierarchical Clustering via Local Search,” May 2024, [Online]. Available: http://arxiv.org/abs/2405.15983
A. Hidayat et al., “Analisis Perancangan Sistem Informasi pada Perpustakaan Institut Teknologi Mitra Gama,” 2021.
A. K. Jain, M. N. Murty, and P. J. Flynn, “Data Clustering: A Review,” 2020.
R. Dila, S. Defit, and S. Arlis, “BULLETIN OF COMPUTER SCIENCE RESEARCH Analisis Algoritma K-Means Clustering Dalam Pengelompokan Prestasi Belajar Siswa Menengah Atas (SMA),” Media Online), vol. 5, no. 5, pp. 1113–1119, 2025, doi: 10.47065/bulletincsr.v5i5.751.
V. Cohen-Addad, V. Kanade, F. Mallmann-Trenn, and C. Mathieu, “Hierarchical Clustering: Objective Functions and Algorithms,” Apr. 2021, [Online]. Available: http://arxiv.org/abs/1704.02147
F. Rahmawati, S. Imanuel Fallo, P. Studi Matematika, and F. Pendidikan Matematika dan Ilmu, “Leibniz: Jurnal Matematika HIERARCHICAL AGGLOMERATIVE CLUSTERING DENGAN METODE WARD UNTUK PEMETAAN PASAR TENAGA KERJA PASCAPANDEMI DI JAWA TENGAH: PENDEKATAN MACHINE LEARNING BERBASIS KLASTERISASI,” 2022.
A. Fadliana and F. Rozi, “PENERAPAN METODE AGGLOMERATIVE HIERARCHICAL CLUSTERING UNTUK KLASIFIKASI KABUPATEN/KOTA DI PROVINSI JAWA TIMUR BERDASARKAN KUALITAS PELAYANAN KELUARGA BERENCANA,” 2020.
P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” 2021.
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