Dina Agustina, Fadhilah Fitri, Zilrahmi, Rara Sandhy Winanda, Devni Prima Sari
The fourth industrial revolution, also known as Industry 4.0, is driven by the combination of IoT, AI, and big data in the manufacturing industry. One of the challenges for manufacturers is machine failures or downtimes, which can significantly hinder production processes. Predictive maintenance (PdM) is a solution to this problem and is widely used in the industry. In this study, Support Vector Machine (SVM) and Random Forest (RF) algorithms were used to predict the Overall Equipment Effectiveness (OEE) of a production machine, and the best model was selected based on accuracy using a confusion matrix. The study involved data preprocessing, exploratory data analysis, feature selection, and training the models to generate predictive classification models. The accuracy of the SVM algorithm was found to be 87%, while the RF algorithm achieved an accuracy of 91%. Therefore, the RF algorithm can be considered a better choice for forecasting OEE using these two features. © 2024 Author(s).
Mathematics Department, Universitas Negeri Padang, Jalan Prof. Dr. Hamka, Air Tawar Padang, Indonesia; Statiscitcs Department, Universitas Negeri Padang, Jalan Prof. Dr. Hamka, Air Tawar Padang, Indonesia; Data Analytics Mathematical Modelling, Forecasting Research Group, Universitas Negeri Padang, Jalan Prof. Dr. Hamka, Air Tawar Padang, Indonesia
