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This research focuses on predictive maintenance of heavy equipment in the palm oil industry using a neural network based on operational data. The study aims to reduce human error in machine selection for maintenance by employing deep learning techniques to classify machines as effective or ineffective, achieving high accuracy rates. The dataset consists of operational data from a palm oil company, addressing challenges such as outliers and class imbalance through various data preprocessing methods.

Author

Putera Farrel Elian

Language

EN