Abstract
Utilizing the team to carry out operational vehicle maintenance activities is crucial to maintaining smooth mobility. Well-scheduled maintenance can prevent unexpected problems and minimize disruption to vehicle operations. The problem is that the implementation of operational vehicle maintenance policies is not yet optimal. The aim of this research is to focus on operational vehicles so that use is not disrupted and mobility runs smoothly, maintenance scheduling is needed. Completion of this research method will use the Naïve Bayes and Decision Tree data mining applications. This research produces a comparison of the two data mining applications to determine maintenance performance with an accuracy level of the Naïve Bayes method of 33.33% and a Decision Tree at 75.00%. The results of the best algorithm performance analysis are used as a reference for implementing vehicle maintenance scheduling.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
