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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Ahmad Kamal
Institut Bisnis dan Teknologi Pelita Indoensia
Computer Science & IT Engineering

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Serendipity-Aware Decision Support System Using Entropy-Weighted Hybrid GA-PSO Ahmad Kamal; Suaini Binti Sura; Lai Po Hung; Renita Astri; Johan Johan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7668

Abstract

The rapid growth of social commerce has intensified competition among online handicraft businesses, making effective store planning increasingly important. While most studies focus on consumer recommendation systems, limited research supports entrepreneurs during the early stage of store configuration. This study proposes a serendipity-aware Decision Support System (DSS) for handicraft store planning using an entropy-weighted hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO). A dataset of 105 handicraft stores in West Sumatra was encoded into 19-bit chromosomes representing materials, product types, location, and digital commerce visibility. Entropy-based weighting objectively determined attribute importance without subjective judgment. GA explored store configurations, while PSO optimized evolutionary parameters to balance preference similarity and serendipitous exploration. The proposed framework generated store configurations superior to those in the original dataset. The best solution achieved a preference similarity score of P(x)=0.9108, outperforming the best existing store (P(x)=0.8513) by 6.99%. The hybrid GA-PSO also showed stable performance across multiple runs, indicating robust convergence. This study contributes a data-driven DSS framework integrating entropy weighting, hybrid GA-PSO optimization, and serendipity-aware exploration for entrepreneurial decision support in social commerce.
Serendipitous Recommendations for Handicraft Store Discovery in Social Commerce Using a Genetic Algorithm with Adaptive Selection Ahmad Kamal; Suaini Binti Sura; Lai Po Hung; Renita Astri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7077

Abstract

In social commerce, particularly among small and medium-sized handicraft enterprises (SMEs), personalized recommender systems (RS) are crucial for enhancing store and product discovery. Conventional content-based filtering (CBF) often overemphasizes accuracy, leading to over-specialization and limiting exposure to novel or diverse items, an issue in the handicraft sector where uniqueness is valued. This study proposes a serendipitous recommendation approach using a Genetic Algorithm (GA) with adaptive selection strategies, Roulette Wheel Selection (RWS), Tournament Selection (TnS), and Rank-Based Selection (RBS), to balance relevance and unexpectedness. Handicraft store attributes, such as product types, materials, and services, are encoded in a 19-bit chromosome and evaluated via a hybrid fitness function. Tested on real data from West Sumatra SMEs, the model is assessed using Precision, Recall, Novelty, and Serendipity metrics. Results show that the GA-based adaptive selection approach outperforms baseline CBF in producing more diverse and surprising recommendations, fostering exploratory shopping experiences and supporting the discovery of unique local products in social commerce ecosystems.
Co-Authors Johan Johan Lai Po Hung Renita Astri Suaini Binti Sura