<![CDATA[Flight Data Recorder (FDR) classification in civil aviation is currently performed manually, requiring operators to interpret complex multi-page regulatory documents. Manual Flight Data Recorder (FDR) classification in civil aviation compliance is a time-consuming, error-prone process that becomes increasingly complex when operators must meet requirements across multiple jurisdictions. This research aims to develop an automated web-based FDR classification system using a rule-based Decision Tree algorithm capable of classifying FDRs under multiple regulatory frameworks, with current implementation covering Indonesia’s Civil Aviation Safety Regulation (CASR) Part 91 and Philippine Civil Aviation Regulations (PCAR) Part 7. The system integrates with the Express Readout Worksummary Database via REST API and automatically processes aircraft parameters Maximum Take-Off Weight (MTOW), manufacturing date, and aircraft type to determine the appropriate FDR type and recording-parameter requirements. Exact-match rules are applied when aircraft data fall within defined regulatory date ranges; MTOW-based approximation is employed for cases outside those ranges. Testing on 742 aircraft from 80 operators yielded 97.5% classification accuracy compared with manual expert methods, reducing per-aircraft processing time from 30 minutes to under 1 second. The system also generates automated compliance reports in Microsoft Word format and provides a customer analytics dashboard for operational insights. These results confirm that the proposed system offers an efficient, consistent, and scalable solution for multi-regulation aviation compliance management, supporting both CASR and PCAR regulatory frameworks.]]>
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