<![CDATA[The development of e-commerce in Indonesia has changed consumer decision-making patterns, particularly on the Shopee platform, which offers a wide selection of products at competitive prices. However, the multitude of alternatives often makes it difficult for consumers to determine the best product that balances price, quality, and seller reputation. This study aims to develop a linear algebra-based product selection optimization model with a linear programming approach to help consumers make rational, data-driven decisions. Four main factors were analyzed: price, shipping costs, ratings, and reviews. Data were obtained through a questionnaire survey of 100 Shopee user respondents and actual product data in the fashion category. The results show that reviews (0.2819) and ratings (0.2698) have a dominant influence compared to price (0.2283) and shipping costs (0.2200). This optimization model is able to transform qualitative variables into measurable quantitative values, resulting in optimal product recommendations based on a balance between digital trust and economic efficiency. This approach provides a unique contribution in applying linear algebra concepts to the analysis of digital consumer behavior on e-commerce platforms.]]>
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