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All Journal Elkom: Jurnal Elektronika dan Komputer
Muhammad Zaki Mubarok
Universitas Indonesia Mandiri
Education

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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 Vol. 19 No. 1 (2026): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v19i1.3732

Abstract

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.
Co-Authors Eko Aziz Apriadi Julianto, Ribut Muawan Bisri