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All Journal Indonesian Journal of Data and Science Journal of System and Computer Engineering Global Abdimas: Jurnal Pengabdian Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science International Journal of Artificial Intelligence in Medical Issues Advances in Computer System Innovation Journal (ACSI Journal)
Syam, Rahmat Fuadi
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Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : Journal of System and Computer Engineering

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Predictive Sparepart Maintenance Menggunakan Algoritma Machine Learning Extreme Gradiant Boosting Regressor Usman, Syahrul; Syam, Rahmat Fuadi
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1418

Abstract

Spare parts are components that make up a single object that has a specific function. In car vehicles, spare parts have the function of maintaining the performance and function of the vehicle. Predictive Spare Part Maintenance is an effort to improve operational efficiency, customer service, and reduce vehicle downtime through the application of analysis and machine learning algorithms to predict spare part replacement times. A machine learning approach can be used to predict maintenance times for car spare parts, where one of the algorithms that can be used is XGBoost Regressor. Through this approach, this research aims to improve service planning by predicting spare part replacement times based on certain indicators, With the implementation of this research, it is hoped that it can increase operational efficiency in automotive after-sales services, increase customer satisfaction, reduce vehicle downtime, and improve overall service planning and most importantly can provide preventive maintenance information to customers. This research provides prediction results with R2-Score values ​​as follows: train data: 93%, Valid: 90%, Test: 90%
Co-Authors Almuhajir Haris, Almuhajir Anirwan Anirwan Arafah, Muhammad Nur Edi Faizal Firman Aziz Ismail Ismail Jeffry Jeffry Kahar Gani Nurdyansa Putra Yuzi Bachmid, Fadel Muhammad Qamal Qamal Rezania Agramanisti Azdy, Rezania Agramanisti Riska Riska Sumardi Sumardi Sumiyatun Syahrul Usman Wijaya, Neti Septi