Model Klastering Hybrid Menggunakan Inisialisasi K-means++ dan Algoritma Optimasi Grey Wolf
DOI:
https://doi.org/10.26418/justin.v13i2.88211Keywords:
KMeans , Grey Wolf Optimizer, Hybrid GWO-KMeans , Klastering, Silhouette Score, Davies-Bouldin IndexAbstract
Penelitian ini mengembangkan GWO-KMeans++, model klastering hybrid yang mengintegrasikan Grey Wolf Optimizer (GWO) dengan K-Means++ untuk mengatasi masalah local optima dalam inisialisasi centroid. Model diuji pada lima dataset UCI (Seeds, Wine, Sonar, Bank, Forest) dengan karakteristik beragam, mulai dari data pertanian berdimensi rendah (6 fitur) hingga sinyal sonar berisik (60 fitur). Kinerja diukur menggunakan Silhouette Score (SC) dan Davies-Bouldin Index (DB) untuk jumlah klaster k=2"“10, lalu dibandingkan dengan K-Means++ melalui Uji Wilcoxon Signed-Rank. Hasil menunjukkan GWO-KMeans++ meningkatkan SC sebesar 19,71"“24,59% (Seeds, k=5"“7), 56,81% (Wine, k=5), dan 210,85% (Sonar, k=2), serta mengurangi DB hingga 22,19% (Seeds, k=7) dan 28,02% (Wine, k=5). Uji statistik mengonfirmasi peningkatan SC signifikan di semua dataset (p < 0,05), dengan nilai p=0,0039 (Seeds, Wine, Sonar), p=0,0117 (Bank), dan p=0,0273 (Forest). Namun, perbaikan DB hanya signifikan pada Seeds (p=0,0117) dan Wine (p=0,0078). Visualisasi klaster memperlihatkan distribusi data lebih terpisah dan centroid lebih akurat, khususnya pada data multidimensi (Wine) dan berisik (Sonar). Model ini stabil pada k=3"“6, cocok untuk data nonlinier, dengan aplikasi di bioinformatika hingga deteksi kecurangan keuangan. Rekomendasi lanjutan meliputi optimasi parameter GWO, integrasi reduksi dimensi (PCA), dan pengujian pada dataset big data.References
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