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All Journal Simtek : Jurnal Sistem Informasi dan Teknik Komputer
Bambang Pramono
Jurusan Informatika, Fakultas Teknik, Universitas Halu Oleo, Kendari, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering

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SISTEM REKOMENDASI PRODUK UMKM KOTA KENDARI BERBASIS WEBSITE DENGAN PENERAPAN ALGORITMA NEURAL COLLABORATIVE FILTERING DAN K-NEAREST NEIGHBOR Muhammad Saharullah Raiya; Jumadil Nangi; La Ode Muhammad Bahtiar Aksara; Bambang Pramono; Isnawaty; Kobajashi Togo Isamu
Simtek : jurnal sistem informasi dan teknik komputer Vol. 11 No. 1 (2026): April 2026
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v11i1.1769

Abstract

The rapid growth of e-commerce has created significant opportunities for MSMEs in Kendari City to expand their market reach digitally; however, product visibility and service personalization remain suboptimal. This study aims to design and develop a web-based product recommendation system for MSMEs by implementing Neural Collaborative Filtering (NCF) and K-Nearest Neighbor (KNN) algorithms through a switching mechanism. KNN is used in cold-start conditions for new users based on product similarity, while NCF is applied when interaction data are available to model the non-linear relationships between users and products. The system was developed using the Rational Unified Process (RUP) method with Python and Flask technologies. Evaluation was conducted through algorithm performance testing using Top-N metrics, User Acceptance Testing (UAT), and white-box testing. The results show that NCF achieved an HR@10 of 0.7692 and an NDCG@10 of 0.4691, while KNN obtained a Precision@10 of 0.9500. UAT produced a score of 82.67%, categorized as very good, and white-box testing indicated that the logical flow of KNN and NCF operated as designed.
Co-Authors Isnawaty Jumadil Nangi Kobajashi Togo Isamu La Ode Muhammad Bahtiar Aksara Muhammad Saharullah Raiya