Keywords
Mixer Trucks
K-NN Algorithm
CRISP-DM
Machine Learning
How to Cite
Abstract
PT. Varia Usaha Beton, a cement product company, faces frequent breakdowns of mixer trucks, reducing reliability from the target 90% to 60%. This study aims to predict truck breakdowns using a machine learning model based on the K-NN algorithm within the CRISP-DM framework. Data from the company's maintenance records were cleaned and split into training and testing sets. With k=20, the model achieved 90% accuracy on training data and 83% on testing data. These results can help improve maintenance scheduling and resource planning, enhancing truck reliability. Future research should compare other algorithms and consider different programming environments.
Highlights:
- High Accuracy: K-NN model achieved 90% training and 83% testing accuracy.
- Maintenance Aid: Improves scheduling and resource planning for truck maintenance.
- Future Research: Compare algorithms and explore different programming environments.
Keywords: Predictive Maintenance, Mixer Trucks, K-NN Algorithm, CRISP-DM, Machine Learning
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