Penerapan Kecerdasan Buatan (AI) dalam Sistem Rekomendasi Produk Pada E-commerce - Muhammad Zaki Mubarok; Eko Aziz Apriadi; Ribut Julianto; Muawan Bisri (Elkom: Jurnal Elektronika dan Komputer, 2026)
The rapid advancement of the digital era has fundamentally transformed the electronic commerce landscape, where the abundance of product options frequently causes information overload that hinders consumer decision-making. This study aims to examine the effectiveness of artificial intelligence implementation in product recommendation systems on e-commerce platforms while comparing the performance of various algorithms employed. The research adopted a comparative experimental quantitative approach using 10,000 transaction records, evaluating three primary algorithms, namely Collaborative Filtering, Content-Based Filtering, and Hybrid Filtering, through Precision, Recall, F1-Score, and RMSE metrics. Findings revealed that AI-based systems achieved an average recommendation relevance rate of 84.6%, substantially surpassing conventional systems at only 51.3%. Among the three algorithms tested, Hybrid Filtering demonstrated the highest performance with an F1-Score of 87.9% and the lowest RMSE of 0.231. The hybrid approach also proved most resilient under cold-start and data sparsity conditions compared to other algorithms. This study concludes that integrating artificial intelligence, particularly through a hybrid algorithm, represents the most optimal strategy for improving product recommendation personalization quality and driving sales conversion on large-scale e-commerce platforms.