Penerapan Algoritma K-Nearest Neighbor (KNN) untuk Personalisasi Produk pada E-Commerce Menggunakan Pendekatan User-User Collaborative Filtering
DOI:
https://doi.org/10.52060/juptik.v4i1.4300Abstract
Pertumbuhan pesat sektor e-commerce menuntut adanya personalisasi untuk memitigasi kelebihan informasi dan mendorong keterlibatan pelanggan. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi sistem rekomendasi menggunakan algoritma K-Nearest Neighbor (KNN) dengan pendekatan User-User Collaborative Filtering. Studi ini menggunakan dataset Online Retail UCI yang mencakup 541.909 transaksi dari peritel berbasis di Inggris. Metodologi penelitian meliputi pembersihan data deterministik, konstruksi matriks interaksi pengguna-item biner, dan penerapan kemiripan kosinus untuk mengidentifikasi lingkungan tetangga. Rekomendasi dihasilkan menggunakan rata-rata tertimbang kemiripan untuk tetangga. Hasil empiris menunjukkan bahwa sistem mencapai Average Precision@5 sebesar 0,0915, Average Recall@5 sebesar 0,0575, dan Hit Rate yang signifikan sebesar 0,3437. Temuan ini mengindikasikan bahwa model KNN dengan 10 tetangga tiga kali lebih akurat dibandingkan dengan model dasar satu tetangga. Hit Rate yang tinggi menunjukkan bahwa sistem ini layak secara strategis untuk meningkatkan penjualan silang (cross-selling) dan ROI pemasaran secara keseluruhan dalam lingkungan ritel dunia nyata.
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Keywords:
algoritma KNN, personalisasi produk, e-commerce, user-user collaborative filtering, sistem rekomendasi
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2026-06-01
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