COMPARISON Analisis Komparatif K-Means dan DBSCAN pada Data Nilai Siswa SMP Bina Bangsa Surabaya
DOI:
https://doi.org/10.33998/processor.2026.21.1.2636Kata Kunci:
Data Mining, Clustering, Algoritma K-Means, Sentiment AnalysisAbstrak
Penelitian ini bertujuan untuk menganalisis dan membandingkan efektivitas algoritma K-Means dan DBSCAN dalam proses clustering data nilai siswa kelas VIII B SMP Bina Bangsa Surabaya. Data yang digunakan terdiri dari 24 siswa dengan tiga mata pelajaran utama, yaitu Matematika, Bahasa Indonesia, dan IPA. Melalui pendekatan kuantitatif dengan metode eksperimen komparatif, penelitian ini memanfaatkan platform Google Colab untuk melakukan pra-pemrosesan, normalisasi data, serta implementasi kedua algoritma. Hasil analisis menunjukkan bahwa metode K-Means optimal pada tiga klaster yang mewakili kemampuan rendah, sedang, dan tinggi dengan dukungan nilai Silhouette Score tertinggi pada k = 3. Sementara itu, algoritma DBSCAN membentuk dua klaster utama, yaitu Remedial (17 siswa) dan Tidak Remedial (6 siswa), serta mendeteksi 2 data noise yang menunjukkan karakteristik nilai berbeda secara signifikan. Temuan ini menegaskan bahwa clustering mampu membantu guru dalam mengidentifikasi kebutuhan pembelajaran secara individual dan merancang intervensi akademik yang lebih tepat sasaran. Secara keseluruhan, penerapan clustering terbukti efektif dalam mendukung manajemen kelas yang heterogen dan meningkatkan efisiensi strategi pembelajaran..
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