PENINGKATAN SKALABILITAS SISTEM REKOMENDASI WEBSITE BERITA MENGGUNAKAN CONTENT-BASED FILTERING DAN K-MEANS
DOI:
https://doi.org/10.55123/storage.v5i1.7651Keywords:
Content-Based Filtering, K-Means Clustering, Skalabilitas, Mean Average Precision, Sistem Rekomendasi BeritaAbstract
Perkembangan internet menghasilkan volume data yang besar termasuk website berita yang memunculkan tantangan pada model rekomendasi Content-based filtering (CBF) dengan kompleksitas komputasi linier O(N). Penelitian ini mengusulkan optimalisasi CBF dengan integrasi dengan algoritma K-Means yang akan menghasilkan partisi data dengan tujuan reduksi biaya komputasi. Digunakan 22.250 artikel berita dari antara news. Penentuan nilai cluster (K) optimal menggunakan elbow method dengan K terbaik K = 6 dan divalidasi dengan Silhouette Score dengan nilai 0,5201, yang mengindikasikan sebaran data baik. Hasil dari pengintegrasian CBF dan K-means menunjukan adanya efisiensi pada response time dan penggunaan memori hingga 6 kali lipat. Namun, ditemukan pula trade-off berupa penurunan Mean Average Precision (MAP) dari 0,89 (konvensional) menjadi 0,77 (K=6), yang masih baik pada kualitas rekomendasi. Selain itu, digunakan threshold dengan nilai 0,30 yang terbukti optimal dalam filter konten yang relevan. Kesimpulan yang dapat diambil dari penelitian adalah penggunaan partisi data mampu memberikan kualitas rekomendasi yang tetap andal namun pada beban komputasi yang ditekan.
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