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All Journal Scientific Journal of Informatics Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Georafflesia : Artikel Ilmiah Pendidikan Geografi Jurnal Ipteks Terapan J SIG (Jurnal Sains Informasi Geografi) JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Ekonomi Jurnal Teknologi Dan Sistem Informasi Bisnis JTIK (Jurnal Teknik Informatika Kaputama) TIN: TERAPAN INFORMATIKA NUSANTARA Majalah Ilmiah UPI YPTK Jurnal Ipteks Terapan : research of applied science and education pendidikan, science, teknologi, dan ekonomi Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Sains dan Teknologi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Pemberdayaan: Publikasi Hasil Pengabdian kepada Masyarakat (JPM ITTC ) Jurnal Sains dan Informatika : Research of Science and Informatic Jurnal Pengabdian Masyarakat Dharma Andalas Jurnal Sistem Informasi dan Informatika
Renita Astri
Universitas Dharma Andalas
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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.
Cluster Analysis Based on McKinsey 7s Framework in Improving University Services Deny Jollyta; Dwi Oktarina; Gusrianty; Renita Astri; Lina Arliana Nur Kadim; Ni Gusti Ayu Dasriani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 1 (2021): October 2021
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2005.51 KB) | DOI: 10.59934/jaiea.v1i1.45

Abstract

The epidemic of Covid-19 has impacted all aspects of human life, including education. Academic and administrative services for academic community are suffering, as a result of the fact that not all universities are able to provide online services to help break the chain of Covid-19 distribution. This is due to a lack of human competencies to use technology and a lack of information technology resources, necessitating the development of new strategies by universities to address these flaws. The goal of this study is to develop a university service strategy based on McKinsey 7s cluster results on the part that is having issues based on questionnaire data. The questionnaire is organized on seven McKinsey elements. The Manhattan distance calculation and the K-Medoids algorithm results demonstrated that the structure, system, skill and staff are all part of elements that clustered in k=2 and has to be addressed in aiding services during the Covid-19 pandemic. The McKinsey 7s showed that universities service enhancements may be achieved by combining clustering techniques and McKinsey framework.
Co-Authors ,, Isnardi A, M. Razi Ahmad Kamal Ahmad Kamal Ahmad Kamal Ahmad Kamal Ahmad Kamal ahmad kamal ahmad kamal, ahmad Anggraini, Putri Anggraini, Putri Ayu Dasriani, Ni Gusti Azman, Heru Aulia Deny Jollyta Dwi Oktarina Faradika Dika Faradika, Faradika Gusrianty Gusrianty Indrayeni, Indrayeni Isnardi , Johan Johan Kadim, Lina Arliana Nur Lai Po Hung Lusia, Siska Marcellino, Anggit Mariyanti, Eka Mazni, Deni Irda Mohammad Abdilla Mulya, Dio Prima Nasrah, Rasidah Randi Yudha Pratama Saputra, David Srihasnita, Rita Suaini Binti Sura Sularno Sularno Sularno Sularno Sularno, Sularno Vitri, Gusni Yanni Suherman Zulfahmi - Zulfahmi Fahmi Zulfahmi Zulfahmi