PENERAPAN METODE USER-BASED COLLABORATIVE FILTERING UNTUK REKOMENDASI PRODUK CLOTHING PADA PLATFORM E-COMMERCE SHOPEE
Abstract
Penelitian ini bertujuan untuk mengimplementasikan metode User-Based Collaborative Filtering (UBCF) dalam sistem rekomendasi produk clothing berbasis data e-commerce. Teknik web scraping menggunakan ekstensi Tamper Monkey (JavaScript) diterapkan untuk mengekstraksi data pengguna dari platform Shopee, termasuk nama pengguna, rating, waktu ulasan, serta deskripsi komentar. Data hasil ekstraksi disimpan dalam format CSV dan diproses menggunakan bahasa pemrograman Python dengan pustaka Pandas, NumPy, Selenium, dan BeautifulSoup. Analisis kemiripan antar pengguna dilakukan menggunakan metode Euclidean Distance untuk menentukan pengguna dengan preferensi serupa. Hasil prediksi rating dihitung menggunakan metode Weighted Sum Prediction dan diuji dengan metrik Mean Absolute Error (MAE) untuk menilai tingkat akurasi. Berdasarkan hasil pengujian, sistem rekomendasi berbasis UBCF mampu memberikan rekomendasi produk dengan tingkat akurasi yang baik dan relevansi tinggi terhadap preferensi pengguna. Dengan demikian, penerapan UBCF pada data e-commerce terbukti dapat meningkatkan pengalaman belanja daring pengguna dan memberikan kontribusi nyata bagi pengembangan sistem rekomendasi yang lebih personal dan adaptif.
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