Diagnosa Kerusakan Bearing Menggunakan Principal Component Analysis (PCA) dan Naïve Bayes Classifier

DOI: https://doi.org/10.21456/vol6iss2pp114-123

Article Info
Submitted: 25-09-2016
Published: 26-12-2016
Section: Research Articles

This research is discussing about the usage of data mining which addressed for bearing fault diagnosis. Bearing was one of the essential parts in industry machinery. Bearing was used to reduce machines frictions or could be a moving component which oppressed each other.  This fault diagnosis can avoid loss and damage of other machines components. This research was started with data preprocessing using wavelet discrete transformation, feature extraction, feature reduction using Principal Component Analysis (PCA), and classification process using Naïve Bayes classifier methods. Naïve Bayes Classifier is a classification method which based on probability and Bayesian theorem. Output of these method shows that Naïve Bayes classification have a good performance which shown by a good accuracy in each data test.


Data mining; Fault Diagnosis; PCA; Naïve Bayes Classification

  1. Dwi Pudyastuti 
    Universitas Diponegoro , Indonesia
    Magister Sistem Informasi
  2. Toni Prahasto 
    Universitas Diponegoro
    Fakultas Teknik
  3. Achmad Widodo 
    Universitas Diponegoro
    Fakultas Teknik

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