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All Journal Indonesian Journal of Innovation Studies
Rachman, Meisya Azzahra
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Computer Science & IT Education Engineering Law, Crime, Criminology & Criminal Justice

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Machine Learning Predicts Truck Breakdowns in Indonesia with 83% Accuracy: Machine Learning Memprediksi Kerusakan Truk di Indonesia dengan Akurasi 83% Rachman, Meisya Azzahra; Sukmono, Tedjo
Indonesian Journal of Innovation Studies Vol. 25 No. 3 (2024): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i3.1156

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
Co-Authors Tedjo Sukmono