CC BY 4.0Mique, Jr., Eusebio L.Madayag, Maveric B.Asejo, Reendhel John P.Bucsit, James V.Gameng, Klarence Jhay G.2026-03-112026-03-112024-12Madayag, M. B., Asejo, R. J. P., Bucsit, J. V., & Gameng, K. J. G. (2024). Crashlytics: Vehicular black spot analysis using association rule mining. [Unpublished Undergraduate Thesis]. Don Mariano Marcos Memorial State University - Mid La Union Campus, City of San Fernando, La Union. Lakasa ti Sirib, DMMMSU Institutional Repository.https://lakasa.dmmmsu.edu.ph/handle/123456789/1174Full textThis study aimed to analyze accident-prone areas in San Fernando City, La Union, Philippines, using a web-based system developed with Association Rule Mining. It provides valuable insights to inform interventions and policies to reduce vehicular accidents in the region. The research follows the CRISP-DM methodology, which includes six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. System development was based on the Rapid Application Development (RAD) model, which enabled iterative prototyping with user feedback. The system integrated the FP-Growth algorithm for Association Rule Mining to identify accident patterns and was coupled with an interactive map for enhanced visualization and decision-making. Usability testing revealed an average System Usability Scale (SUS) score of 87.14, indicating "Best Imaginable" usability and high user satisfaction.ix, 66 p.: ill. (col.).EnglishAssociation rule miningTraffic accidentsTraffic accidents--ResearchTraffic accidents--Mathematical modelsDrinking and traffic accidents--PreventionDrinking and traffic accidents--Prevention--Technological innovationsVehicular ad hoc networks (Computer networks)Thesis