<![CDATA[The feasibility of laboratory equipment is a crucial factor in ensuring the quality and safety of practical and research activities. At the Faculty of Mathematics and Natural Sciences, Universitas Andalas, the classification of equipment feasibility is still performed manually, leading to inefficiencies and potential assessment errors. This study aims to develop and implement a web-based information system for classifying the feasibility of laboratory equipment using the Naïve Bayes algorithm. The research applies a system development approach following the Waterfall model, starting with requirement analysis, system modeling using Context Diagram, DFD, and ERD, and implementation using PHP and MySQL. The Naïve Bayes algorithm calculates the probability of each feasibility class based on attributes such as procurement year, usage level, damage condition, usage duration, and accessory status. Test results indicate that the system successfully classifies equipment into three categories serviceable, needs repair, and unserviceable with 95% acceptable accuracy. The system generates report-based outputs that support strategic decision-making in equipment maintenance and procurement planning. Thus, this information system is expected to enhance the efficiency and objectivity of laboratory asset management through a data-driven and measurable digital platform.]]>
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