Predictive Maintenance on Paving Machine Using Support Vector Machine (SVM)
Predictive Maintenance Pada Mesin Paving Menggunakan Support Vector Machine (SVM)
DOI:
https://doi.org/10.21070/ups.10349Keywords:
Predictive Maintenance, Support Vector Machine (SVM), Naive Bayes, Paving Machine, ;Abstract
Predictive maintenance is a strategy that uses machine operational data to predict potential damage before it occurs, thereby reducing downtime and maintaining the availability of production equipment. In 2024, PT XYZ experienced 342.75 hours of downtime with 82.30% availability and an output of 3,950,661 units, requiring more structured maintenance planning to minimize disruptions. This study aims to utilize predictions of the downtime duration of a paving machine's hydraulic system to develop a more efficient preventive maintenance schedule. The methods used were linear Support Vector Machine (SVM) and Multinomial Naive Bayes to predict downtime duration, with SVM achieving an accuracy of 89,1% with an MSE of 0,109 and Multinomial Naive Bayes achieving 85,5% with 0,145.
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