Data Pattern Of Computer Maintenance Management System With Eclat Algorithm

Farid Sukmana; Fahrur Rozi

doi:10.22219/kinetik.v4i4.792

Issue

Vol 4, No 4, November 2019

Issue Published : Oct 30, 2019

Data Pattern Of Computer Maintenance Management System With Eclat Algorithm

https://doi.org/10.22219/kinetik.v4i4.792

Farid Sukmana

Universitas Muhammadiyah Gresik

Fahrur Rozi

STKIP PGRI Tulungagung

Corresponding Author(s) : Farid Sukmana

farid.mukmin@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol 4, No 4, November 2019
Article Published : Oct 30, 2019

Abstract

Decision support system, basically used to help choosing some solution for stakeholde to take the best decision in manufacturer. In manufacturer company using Enterprise Resource System (ERP) that has Work Oder (WO) modul as request maintenance from user. But many of data from WO still didn’t use to help decision making and only as warehouse data about infrastructure maintenance from last time. Because that, author use that data to help technician to take decision making by using association rule as pattern processing. This is because WO has unique pattern that has problem (p), symptom (s), and root cause (r). Previous research (Sukmana, Rozi, 2017) was proved if association rule can use to help people to take decison making, it is just involved two variable, that is symptom (s), root cause (r) and using apriori algorithm as association rule. And focussing in this research is using that three variable and eclat algorithm as association rule methode. Result of this research has purpose to take the best pattern when using eclat algorithm.

Keywords

Association Rule Eclat Algorithm

Sukmana, F., & Rozi, F. (2019). Data Pattern Of Computer Maintenance Management System With Eclat Algorithm. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 4(4), 357-366. https://doi.org/10.22219/kinetik.v4i4.792

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References

Butcher S.W. “Assessment of Condition-Based Maintenance in the Department of Defense”, Logistic Management Institute, Virginia. 2000.
Zaied, R.A., Abhary K., and Gomaa A.H. “Intelegent Integrated Maintenance of Manufacturing System”. Engineering the Future. 2010, 1: 297-316.
Fouad R.H, Samhouri M. “A Fuzzy Logic Approach for Scheduling Preventive Maintenance in ERP”. Industrial Engineering Department, Hashimete University.
Roth M.R, Denis A, Wixom B.H. System Analysis and Design, 5th edition. John Wiley & Sons, Inc, Singapore. 2008.
Martinez H., Laukkanen S., Mattila J. “A New Hybrid Approach for Augmented Reality Maintenance in Scientic Facilities”. International Journal of Advanced Robotic System. 2013, 10:321.
Rozi F., Sukmana F. “Document Grouping by Using Meronyms and Type-2 Fuzzy Association Rule Mining”. Journal of ICT Research and Applications. 2017, 11: 268-283.
Rozi, F., Sukmana F. “Metode Siklis dan Adaptive Neuro Fuzzy Inference System untuk Peramalan Cuaca”. Jurnal Ilmiah Penelitian dan Pembelajaran Informatika (JIPI). 2016: 1.
Rozi, F., Sukmana F. “Penggunaan Moving Average Dengan Metode Hybrid Artificial Neural Network Dan Fuzzy Inference System Untuk Prediksi Cuaca”. Jurnal Ilmiah Penelitian dan Pembelajaran Informatika (JIPI). 2016:2.
Ibrahim VM, Malek ZA, Muhammad NA. “Status Review on Gas Insulated Switchgear Partial Discharge Diagnostic Technique for Preventive Maintenance”. Indonesian Journal of Electrical Engineering and Computer Science. 2017. Vol. 7, No 1:9-17.
Abdullah L. “Identifying Risk Factors of Diabetes using Fuzzy Inference System”. IAES International Journal of Artificial Intelligence (IJ-AI). Vol.6, No.4:150-158
Sukmana, F., Rozi, F. “Decision Support System On Computer Maintenance Management System Using Association Rule and Fisher Exact Test One Side P-Value”. Telecommunication, Computing, Electronics and Control (TELKOMNIKA). 2017, 15:4.
Sukmana, F., Rozi, F. “Rekomendasi Solusi pada Computer Maintenance Management System Menggunakan Association Rule, Fisher Exact Test One Side P-value dan Double One Side”. Jurnal Teknologi Informasi dan Ilmu Komputer, 2017, 4: 213-220.
Tan, P.N, Steinbach M and Kumar V. Introduction Data Mining, 1st edition. Pearson Education, Inc., Boston. 2006.
Agrawal R, Imielinski T, Swami A. “Mining Assocation Rules between Sets of Items in Large Database”. Proceeding of the 1993 ACM SIGMOOD Conference, Computer Science Department Rutgers University. 1993.
Saran, Aditi. Association Rule Mining, Data Mining Technique and Tools for Knowledge Discovery in Agricultural Dataset. 298.
Pawlak R.R. Industrial Problem Solving Simplified An 8-Step Program. Springer Since + Business Media, New York. 2014.
Han J., Pei J., Yin Y. “Mining Frequent Patterns without Candidate Generation”. School of Computing Science. Simon Fraser University. 2000 : Paper ID : 196
Mandave P., Mane M., Patil S. “Data Mining Using Association Rule Based on Apriori algorithm and improved with illustration”. International Journal of Latest Trends in Engineering and Technology (IJTET). 2003, 3:107.
Lai K., Cerpa N. “Support vs Confidence in Association Rule Algorithm”. School of Computer Science and Engineering, University of new South Wales and Narciso Cerpa, University of Talca.
Verma A, Khan S.D, Maiti J, Khrisna O.B. “Identifying Pattern of Safety Related Incident in a Steel Plant Using Association Rule Mining of Incident Investigation Report”. Safety Science. 2014, 70: 89-98.
Heaton J. Comparing Dataset Characteristics that Favor the Apriori, Eclat or FP-Growth Frequenct Itemset Mining Algorithm. Cornell University Library . 2017.
Agrawal R., Srikant R. et al., “Fast algorithms for mining association rules,” in Proceedings of the 20th international conference of very large data bases, VLDB, vol. 1215, 1994, pp. 487–499.
Blazewicz J, Kubiak W, Morzy T, Rusinkiewicz M. Handbook on Data Management in Information System. 2003. Springer Science and Business Media. Berlin Heidelberg. 1st edition : 504-505.
Ma Z, Yang J, Zhang T, Liu F. An Improved Eclat Algorithm for Mining Association Rules Based on Increased Search Strategy. International Journal of Database Theory and Application. 2016. Vol. 9 : 251-266.

References

Butcher S.W. “Assessment of Condition-Based Maintenance in the Department of Defense”, Logistic Management Institute, Virginia. 2000.

Zaied, R.A., Abhary K., and Gomaa A.H. “Intelegent Integrated Maintenance of Manufacturing System”. Engineering the Future. 2010, 1: 297-316.

Fouad R.H, Samhouri M. “A Fuzzy Logic Approach for Scheduling Preventive Maintenance in ERP”. Industrial Engineering Department, Hashimete University.

Roth M.R, Denis A, Wixom B.H. System Analysis and Design, 5th edition. John Wiley & Sons, Inc, Singapore. 2008.

Martinez H., Laukkanen S., Mattila J. “A New Hybrid Approach for Augmented Reality Maintenance in Scientic Facilities”. International Journal of Advanced Robotic System. 2013, 10:321.

Rozi F., Sukmana F. “Document Grouping by Using Meronyms and Type-2 Fuzzy Association Rule Mining”. Journal of ICT Research and Applications. 2017, 11: 268-283.

Rozi, F., Sukmana F. “Metode Siklis dan Adaptive Neuro Fuzzy Inference System untuk Peramalan Cuaca”. Jurnal Ilmiah Penelitian dan Pembelajaran Informatika (JIPI). 2016: 1.

Rozi, F., Sukmana F. “Penggunaan Moving Average Dengan Metode Hybrid Artificial Neural Network Dan Fuzzy Inference System Untuk Prediksi Cuaca”. Jurnal Ilmiah Penelitian dan Pembelajaran Informatika (JIPI). 2016:2.

Ibrahim VM, Malek ZA, Muhammad NA. “Status Review on Gas Insulated Switchgear Partial Discharge Diagnostic Technique for Preventive Maintenance”. Indonesian Journal of Electrical Engineering and Computer Science. 2017. Vol. 7, No 1:9-17.

Abdullah L. “Identifying Risk Factors of Diabetes using Fuzzy Inference System”. IAES International Journal of Artificial Intelligence (IJ-AI). Vol.6, No.4:150-158

Sukmana, F., Rozi, F. “Decision Support System On Computer Maintenance Management System Using Association Rule and Fisher Exact Test One Side P-Value”. Telecommunication, Computing, Electronics and Control (TELKOMNIKA). 2017, 15:4.

Sukmana, F., Rozi, F. “Rekomendasi Solusi pada Computer Maintenance Management System Menggunakan Association Rule, Fisher Exact Test One Side P-value dan Double One Side”. Jurnal Teknologi Informasi dan Ilmu Komputer, 2017, 4: 213-220.

Tan, P.N, Steinbach M and Kumar V. Introduction Data Mining, 1st edition. Pearson Education, Inc., Boston. 2006.

Agrawal R, Imielinski T, Swami A. “Mining Assocation Rules between Sets of Items in Large Database”. Proceeding of the 1993 ACM SIGMOOD Conference, Computer Science Department Rutgers University. 1993.

Saran, Aditi. Association Rule Mining, Data Mining Technique and Tools for Knowledge Discovery in Agricultural Dataset. 298.

Pawlak R.R. Industrial Problem Solving Simplified An 8-Step Program. Springer Since + Business Media, New York. 2014.

Han J., Pei J., Yin Y. “Mining Frequent Patterns without Candidate Generation”. School of Computing Science. Simon Fraser University. 2000 : Paper ID : 196

Mandave P., Mane M., Patil S. “Data Mining Using Association Rule Based on Apriori algorithm and improved with illustration”. International Journal of Latest Trends in Engineering and Technology (IJTET). 2003, 3:107.

Lai K., Cerpa N. “Support vs Confidence in Association Rule Algorithm”. School of Computer Science and Engineering, University of new South Wales and Narciso Cerpa, University of Talca.

Verma A, Khan S.D, Maiti J, Khrisna O.B. “Identifying Pattern of Safety Related Incident in a Steel Plant Using Association Rule Mining of Incident Investigation Report”. Safety Science. 2014, 70: 89-98.

Heaton J. Comparing Dataset Characteristics that Favor the Apriori, Eclat or FP-Growth Frequenct Itemset Mining Algorithm. Cornell University Library . 2017.

Agrawal R., Srikant R. et al., “Fast algorithms for mining association rules,” in Proceedings of the 20th international conference of very large data bases, VLDB, vol. 1215, 1994, pp. 487–499.

Blazewicz J, Kubiak W, Morzy T, Rusinkiewicz M. Handbook on Data Management in Information System. 2003. Springer Science and Business Media. Berlin Heidelberg. 1st edition : 504-505.

Ma Z, Yang J, Zhang T, Liu F. An Improved Eclat Algorithm for Mining Association Rules Based on Increased Search Strategy. International Journal of Database Theory and Application. 2016. Vol. 9 : 251-266.

Issue

Vol 4, No 4, November 2019

Data Pattern Of Computer Maintenance Management System With Eclat Algorithm

Corresponding Author(s) : Farid Sukmana

Abstract

Keywords

Download Citation

References

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