ANALISIS PEMELIHARAAN PREDIKTIF KENDARAAN OPERASIONAL MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN DECISION TREE

Authors

DOI:

https://doi.org/10.35817/publicuho.v7i2.412

Keywords:

Body Repair, Decision Tree, General Repair, Naïve Bayes, Periodic Maintenance, Vehicle maintenance

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.

References

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.

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

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

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

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.

Gupta, S., Pienta, R., Tamersoy, A., Chau, D. H., & Basole, R. C. (2015). Identifying Successful Investors in the Startup Ecosystem. Proceedings of the 24th International Conference on World Wide Web, 39–40. https://doi.org/10.1145/2740908.2742743

Hastuti, K. (2012). Analisis Komparasi Algoritma Klasifikasi Data Mining untuk Prediksi Mahasiswa Non Aktif. Seminar Nasional Teknologi Informasi & Komunikasi Terapan 2012, 14(1), 241–249

Jain, M., Vasdev, D., Pal, K., & Sharma, V. (2022). Systematic literature review on predictive maintenance of vehicles and diagnosis of vehicle's health using machine learning techniques. Computational Intelligence, 38(6), 1990-2008.

Massaro, A., Selicato, S., & Galiano, A. (2020). Predictive maintenance of bus fleet by intelligent smart electronic board implementing artificial intelligence. IoT, 1(2), 12.

Prakash P. Shenoy dan Lili Sun. Using bayesian networks for bankruptcy prediction : Some methodological issues. In European Journal of Operational Research, volume 18, pages 738– 753, 2007.

Shodiqin, H. (2022). Sustainable Maintenance Melalui Prediksi Preventive Maintenance di Plant Cold Roll Mills (CRM) PT Krakatau Steel (Persero) Tbk dengan Algoritma Naïve Bayes Classifier dan Decision Tree. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 9(2), 876-890

Subqi, F. M., & Anggraini, D. (2021). Data Mining Untuk Pemeliharaan Prediktif Mesin Produksi berdasarkan Database Kerusakan Mesin menggunakan Naïve Bayes

Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability engineering & system safety, 215, 107864.

Downloads

Published

2024-05-20