BibTex Citation Data :
@article{JSINBIS12097, author = {Dwi Pudyastuti and Toni Prahasto and Achmad Widodo}, title = {Diagnosa Kerusakan Bearing Menggunakan Principal Component Analysis (PCA) dan Naïve Bayes Classifier}, journal = {Jurnal Sistem Informasi Bisnis}, volume = {6}, number = {2}, year = {2016}, keywords = {Data mining; Fault Diagnosis; PCA; Naïve Bayes Classification}, abstract = { 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. }, issn = {2502-2377}, pages = {114--123} doi = {10.21456/vol6iss2pp114-123}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/12097} }
Refworks Citation Data :
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.
Article Metrics:
Last update:
Last update: 2024-12-21 21:04:27
Authors who submit the manuscripts to Journal JSINBIS must understand and agree that if the manuscript is accepted for publication, the copyright of the article belongs to JSINBIS and Diponegoro University as the journal publisher.
Copyright includes the exclusive right to reproduce and provide articles in all forms and media, including reprints, photographs, microfilm and any other similar reproductions, as well as translations. The author reserves the rights to the following:
JSINBIS and Diponegoro University and the Editors make every effort to ensure that no false or misleading data, opinions or statements are published in this journal. The content of articles published in JSINBIS is the sole and exclusive responsibility of the respective authors.
Copyright transfer agreement can be found here: [Copyright transfer agreement in doc] and [Copyright transfer agreement in pdf].
JSINBIS (Jurnal Sistem Informasi Bisnis) is published by the Magister of Information Systems, Post Graduate School Diponegoro University. It has e-ISSN: 2502-2377 dan p-ISSN: 2088-3587 . This is a National Journal accredited SINTA 2 by RISTEK DIKTI No. 48a/KPT/2017.
Journal JSINBIS which can be accessed online by http://ejournal.undip.ac.id/index.php/jsinbis is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats