Development of Emotion Stress Relief Recommender Tool Using Machine Learning

M Mohammed Mustafa

Abstract


UnilaHub, a mobile e-commerce application developed by the University of Lampung, facilitates transactions for products derived from university research. This study aims to enhance UnilaHub by introducing new features like facility rental, product shipment tracking, and integrated payment systems. The research consisted of three main phases: problem identification, application development (prototyping method), and system testing. Utilizing the prototyping method, the researchers iteratively developed these features, incorporating user feedback to ensure functionality and usability. System testing, encompassing User Acceptance Testing (UAT) and Black Box Testing, validated the effectiveness of these enhancements. High user satisfaction indices were recorded: 84.19 percent for the Web Administrator, 84.66 percent for the Public Apps, and 82.62 percent for the Research Apps. The results indicate that the new features significantly meet user needs and improve the overall user experience. The research is expected to improve the efficiency and satisfaction of UnilaHub users through the development of facility rental, product delivery tracking, and in-app payments. Integrating payment gateway and delivery tracking through third-party services has improved transaction efficiencies and user satisfaction, contributing to the digitization and centralization of administrative processes in UnilaHub.


Keywords


API; Face Detection; Image Processing; Machine Learning; Stress

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References


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DOI: http://dx.doi.org/10.24042/ijecs.v4i1.21593

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