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Kaila, Afifah Syifah
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Computer Science & IT Electrical & Electronics Engineering

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Platform An E-Commerce Platform for Coffee MSMEs: System Design and Basic Features Hesti, Emilia; Kaila, Afifah Syifah; Handayani, Ade Silvia; Novianti, Leni; Rakhman, M Arief
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2756

Abstract

Digitalization of Micro, Small, and Medium Enterprises (MSMEs) has emerged as a strategic necessity in the era of digital transformation. However, many coffee-based MSMEs in Indonesia continue to rely on third-party marketplace platforms that limit autonomy over customer data, pricing control, and brand personalization. This study aims to address these constraints by designing and developing an independent, web-based e-commerce system that aligns with the specific operational needs of coffee MSMEs particularly those seeking low-cost, user-friendly solutions that enable direct customer engagement and reduce commission-based dependencies. The system was developed using Laravel for the backend and Vite.js for the frontend, adhering to the sequential stages of the waterfall model: requirements analysis, system design, implementation, and testing. Key features include product catalog management, shopping cart functionality, manual payment upload, and product review integration. Black-box testing confirmed that all features operated without critical errors under typical usage conditions. Usability testing conducted with five MSME users resulted in an average satisfaction score of 4.23 out of 5 (83%), with high ratings for ease of navigation and interface responsiveness. Performance metrics, including average page load time (<=3 seconds), device compatibility, and user flow scalability, met expected standards. Although the current system employs manual payment validation, future enhancements will focus on integrating secure payment gateways, real-time analytics dashboards, and modular APIs. In summary, the platform offers a practical and scalable e-commerce solution tailored to the autonomy and contextual demands of Indonesia's coffee MSMEs.
Personalized Product Recommendations Using Restricted Boltzmann Machines To Overcome Cold-Start Challenges On A Niche Coffee E-Commerce Platform Hesti, Emilia; Handayani, Ade Silvia; Suzanzefi, Suzanzefi; Agung, Muhammad Zakuan; Rosita, Ella; Asriyadi, Asriyadi; Kaila, Afifah Syifah; Afifah, Luthfia; Ardiansyah, M.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1551

Abstract

This paper examines the use of a Restricted Boltzmann Machine (RBM) to provide personalized product recommendations on a niche coffee e-commerce platform facing cold-start conditions. We train RBM variants on a binary transaction matrix derived from 100 simulated user transactions and evaluate four hidden-unit configurations (3, 5, 10, 15) using 5-fold cross-validation. Models were trained with Contrastive Divergence (CD-1) and assessed primarily by Mean Squared Error (MSE) for reconstruction fidelity, complemented by ranking metrics (Precision@3, NDCG@3). The 10-hidden-unit configuration achieved the best balance of reconstruction and ranking performance, with an average test MSE ? 0.0454, outperforming popular-item (MSE: 0.0802) and random (MSE: 0.0760) baselines. While the RBM demonstrates strong capability in modeling latent user preferences under sparse data, ranking metrics expose limitations when predicting exact top-N items in extremely sparse cases. The study highlights practical implications for early-stage niche marketplaces and suggests integrating content signals or hybridization to further improve top-N recommendation quality.
Co-Authors Ade Silvia Handayani Afifah, Luthfia Agung, Muhammad Zakuan Asriyadi Asriyadi Emilia Hesti Leni Novianti M. Ardiansyah Rakhman, M Arief Rosita, Ella suzan zefi