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All Journal JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science
Endang Lestari Ruskan
Universitas Sriwijaya, Palembang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perancangan UI/UX Fitur Asrama Mahasiswa Berbasis Website dengan Pendekatan User Centered Design Fika Febrika; Pacu Putra Suarli; Nabila Rizky Oktadini; Allsela Meiriza; Putri Eka Sevtiyuni; Endang Lestari Ruskan; Dedy Kurniawan
JURIKOM (Jurnal Riset Komputer) Vol 10, No 3 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i3.6154

Abstract

The rapid development of information technology makes easier for human accessing and exchanging information through digital media like websites. Since 2019, Badan Pengelola Usaha Universitas Sriwijaya started developing student dormitories booking system, namely Fitur Asrama Mahasiswa. This system can be accessed by Sriwijaya University students via bpu.unsri.ac.id. The types of dormitories shown in this feature consist of apartments, flats, and government dormitories. Based on interviews, it was stated that users still had difficulty accessing Fitur Asrama Mahasiswa, especially in user interface and user experience, so it was necessary to improve interface design with aim of making it easier for users to find information and book dorm room. Design improvement of Fitur Asrama Mahasiswa bpu.unsri.ac.id interface carried out through design process usingUser Centered Design (UCD) approach, so thataccommodate user needs. Design process begins with commitment from bpu unsri to involve users in design process through discussion. Then, determine the context of use through interviews, determine user needs and organizational needs through usability testing and interviews, interface designs based on user needs, and evaluate designsthrough usability testing. Using the UCD approach proven that system usability increased which consists of four parameters, namely success rate, efficiency, error rate, and satisfaction. Success rate increased from 52.86% to 94.76%. Efficiency increased from 45.12% to 96.05%. The error rate decreased from 50.12% to 3.69&. Satisfaction increased from 32.5 to 87.33
Comparative Customer Segmentation Pipelines for E-Commerce Using K-Means-KNN and UMAP-K-Means-XGBoost Dzidan Aditya Gumilang; Endang Lestari Ruskan; Ardina Ariani; Ken Dhita Tania; Ahmad Rifai
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10794

Abstract

The rapid expansion of e-commerce has generated massive volumes of customer data that remain underutilized for supporting Customer Relationship Management (CRM) strategies. Conventional customer segmentation approaches commonly employ a pipeline consisting of K-Means clustering followed by K-Nearest Neighbors (KNN) classification. However, this approach exhibits limitations in handling high-dimensional data and maintaining classification performance on large-scale datasets. This study presents a comparative analysis of two customer segmentation pipelines: the conventional K-Means-KNN pipeline and the proposed Uniform Manifold Approximation and Projection (UMAP)-K-Means-XGBoost pipeline. The experiments were conducted using the E-Commerce Shopper Behavior & Lifestyle dataset, comprising approximately one million customer records and eight selected features representing transactional, psychographic, and financial behavioral characteristics. Clustering performance was evaluated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, while classification performance was assessed using accuracy, precision, recall, and F1-score. Experimental results demonstrate that incorporating UMAP improves cluster separability by preserving the intrinsic structure of high dimensional data, whereas XGBoost consistently outperforms KNN in downstream classification, achieving an accuracy exceeding 99%. These findings indicate that the UMAP-K-Means-XGBoost pipeline provides a more robust, scalable, and interpretable framework for customer segmentation, thereby offering more reliable decision support for data-driven CRM strategies in e-commerce environments.
Co-Authors Ahmad Rifai Allsela Meiriza Ardina Ariani Dedy Kurniawan Dzidan Aditya Gumilang Fika Febrika Ken Dhita Tania Oktadini, Nabila Rizky Pacu Putra Putri Eka Sevtiyuni