Crashlytics:

Simple item page
creativework.keywordsassociation rule mining, traffic accident, vehicular black spot analysis
dc.contributor.advisorMique, Jr., Eusebio L.
dc.contributor.authorMadayag, Maveric B.
dc.contributor.authorAsejo, Reendhel John P.
dc.contributor.authorBucsit, James V.
dc.contributor.authorGameng, Klarence Jhay G.
dc.contributor.chairSapuay-Guillen, Sheena I.
dc.contributor.committeememberLedda, Mark Kristian C.
dc.contributor.committeememberMalicdem, Alvin R.
dc.date.accessioned2026-03-11T03:29:56Z
dc.date.available2026-03-11T03:29:56Z
dc.date.issued2024-12
dc.descriptionFull text
dc.description.abstractThis 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.
dc.format.extentix, 66 p.: ill. (col.).
dc.identifier.citationMadayag, 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 [Unpublished Undergraduate Thesis].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.
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/1174
dc.language.isoEnglish
dc.publisherDon Mariano Marcos Memorial State University – Mid La Union Campus
dc.rights.licenseCC BY 4.0
dc.sdgSDG 11
dc.subjectAssociation rule mining
dc.subjectTraffic accidents
dc.subjectTraffic accidents--Research
dc.subjectTraffic accidents--Mathematical models
dc.subjectDrinking and traffic accidents--Prevention
dc.subjectDrinking and traffic accidents--Prevention--Technological innovations
dc.subjectVehicular ad hoc networks (Computer networks)
dc.titleCrashlytics:
dc.title.alternativeVehicular black spot analysis using association rule mining
dc.typeThesis
dcterms.accessRightsOpen access
thesis.degree.disciplineCollege of Information Technology
thesis.degree.levelUndergraduate
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
UT-MLUC-2024-CIT-BSIT-MadayagMB-FT.pdf
Size:
36.8 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Undergraduate Theses