机器学习预测印度尼西亚卡车故障的准确率高达 83

Indonesian Journal of Innovation Studies Pub Date : 2024-06-10 DOI:10.21070/ijins.v25i3.1156

Meisya Azzahra Rachman, Tedjo Sukmono

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引用次数: 0

摘要

PT.Varia Usaha Beton 是一家水泥制品公司，其搅拌车故障频发，可靠性从目标值 90% 降至 60%。本研究旨在使用 CRISP-DM 框架内基于 K-NN 算法的机器学习模型预测卡车故障。对来自公司维护记录的数据进行了清理，并将其分为训练集和测试集。当 k=20 时，模型在训练数据上的准确率达到 90%，在测试数据上的准确率达到 83%。这些结果有助于改进维护调度和资源规划，提高卡车的可靠性。未来的研究应比较其他算法，并考虑不同的编程环境。亮点高精确度：K-NN 模型的训练准确率达到 90%，测试准确率达到 83%。辅助维护：改进了卡车维护的调度和资源规划。未来研究：比较算法并探索不同的编程环境。关键词预测性维护、搅拌车、K-NN 算法、CRISP-DM、机器学习

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Machine Learning Predicts Truck Breakdowns in Indonesia with 83% Accuracy

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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Indonesian Journal of Innovation Studies

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