This study explores an artificial intelligence (AI)-based predictive maintenance system for industrial machinery in Indonesian manufacturing. By utilizing machine learning algorithms, the system can analyze real-time machine data to predict equipment failures and recommend timely maintenance actions. The implementation of predictive maintenance has shown to reduce machine downtime by 20% and improve operational efficiency in manufacturing plants in Jakarta and Surabaya. This paper discusses the technical design of the predictive maintenance system, its economic impact on production costs, and implications for Indonesia's industrial sector.
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