Author:
Islamia Nuraini ,Arnowo Hari Wibowo ,Muhammad Asrol ,Taufik ,Dyah Lang Wilis
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.
Publisher
Universitas Halu Oleo - Jurusan Ilmu Administrasi Publik
Reference13 articles.
1. AlGanem, H. S., & Abdallah, S. (2022). Exploring the Hidden Patterns Data to Predict Failures of Heavy Vehicles. In Recent Innovations in Artificial Intelligence and Smart Applications (pp. 171-187). Cham: Springer International Publishing.
2. Blank, S. (2013, May). Why the Lean Start-Up Changes Everything. Harvard Business Review. https://hbr.org/2013/05/why-the-lean-start-up-changes-everything
3. Dellermann, D., Ebel, P., Lipusch, N., Popp, K. M., & Leimeister, J. M. (2017). Finding the Unicorn: Predicting Early Stage Startup Success Through a Hybrid Intelligence Method. International Conference on Information Systems (ICIS), 1–12. https://doi.org/https://dx.doi.org/10.2139/ssrn.3159123
4. Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211
5. Glupker, J., Nair, V., Richman, B., Riener, K., & Sharma, A. (2019). Predicting investor success using graph theory and machine learning. Journal of Investment Management, 17(1), 92– 103.