Perancangan Smart Predictive Maintenance untuk Mesin Produksi - Krisman Yusuf Nazara (Politeknik Statistika STIS, 2022)
Indonesia's economic growth rate gets a large contribution from the manufacturing industry. In the industrial era 4.0, optimizing the use of information technology supports the effectiveness of an industry's employee performance to be more productive. To optimize maintenance costs and monitor production equipment and machines, Internet of Things (IoT) technology is needed that is equipped with machine learning to produce smart predictive maintenance. This study aims to obtain the best predictive model for the classification of production machine conditions by comparing various machine learning models. This predictive maintenance model is expected to be able to predict machine maintenance schedules so as to increase the life of production machines, helping to estimate maintenance costs. The analytical method used refers to classification analysis by comparing 6 (six) classification models, namely: XGBoost, k-nearest neighbors, logistic regression, gradient boosting, decision tree regression and random forest. Comparison of these algorithms to obtain the best classification model in the case of production machines. Of the six algorithms, the best model is obtained from XG-boost with an accuracy of 99.07.