Downtime Data Classification Using Naïve Bayes Algorithm on 2008 ESEC Engine

In operational production activities, the data logger of a machine produces piles of diagnostic history data that can be utilized to make it easier to determine maintenance planning. Data mining is the process of identifying patterns from data sets in a large data stack to be able to produce valuable data. Processing data mining has the potential to carry out the process of selecting the type of downtime according to predefined attributes. By using the Naïve Bayes Algorithm, it is expected that the data classification process can be performed appropriately. The data generated in the form of information contains a sequence of problem events that often appear in tables and graphs. This study will discuss how to apply data mining to diagnostic history data using the Naïve Bayes Algorithm. Testing the diagnostic history data using Rapid miner achieves up to 100% accuracy.