Penerapan Supervised Emerging Patterns Untuk Multi Atribut Pada Data Online Izin Usaha Pertambangan di Indonesia (Studi Kasus: EITI Indonesia)
Abstract
Abstract
Indonesian EITI (Extractive Industries Transparency Initiative) is an organization under Ministry of Economic Coordination which used to increase the transparency of extractive industry in Indonesia. EITI Indonesia manage a lot of data about mining, one of the managed data is data Mining Business License in Indonesia. The data has many records that require large storage allocation and difficult process data that is used by the EITI for decision making. .This data Mining Business License will be used for the processing of data mining that aims to help look for interesting patterns to determine a learning and two itemsets (attributes) that exist. Application Data Mining with Emerging Patterns Supervised methods will be used as a solution for managing data such large, so it is easy to produce a decision in the form of an interesting pattern information to determine the transparency of mining license in Indonesia. System development methods using CRISP-DM. The design of data mining applications using the programming language Java, NetBeans and MySQL database tools used to build a technology Supervised Emerging Patterns in multi-attribute decision making.
Keywords
References
Daniel T. Larose, Discovering Knowledge In Data, An Introduction To Data Mining. America: 2005.
Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: discovering trends and differences. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 43–52). San Diego, CA: ACM Press.
Dong, G., Zhang, X., Wong, L., & Li, J. (1999). Classification by aggregating emerging patterns. Proceedings ofthe Second International Conference on Discovery Science (pp. 30–42). Tokyo, Japan: Springer-Verlag.
Fan, H. (2004). Efficient mining of interesting emerging patterns and their effective use in classification, PhD thesis, The Department of Computer Science and Software Engineering,
University of Melbourne.
Guozhu Dong dan Jinyan Li., Mining border descriptions of emerging patterns from dataset pairs. Knowledge and Information Systems. USA, Singapore, 1999, vol. 8.
Jinyan Li, Guozhu Dong, and Kotagiri Ramamohanarao. Instance-based classification by emerging patterns. In Proceedings of the 14th European Conference on Principles and Practice ofKnowledge Discovery in Databases (PKDD-2000), pages 191–200, 2000.
Jinyan Li and Limsoon Wong. Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. Bioinformatics, 18(10):1406–1407, 2002.
John Wiley dan Sons, Ltd., A Practical Guide to Data Mining for Business and Industry”. Germany: 2014.
Josef Bernadi,Suharjito, (2016). Executive Information system modelling to monitor Indonesian criminal rate, CommIT journal, 10(1), pp. 1-7.
Kdnuggets website. [Online]. Available: http://www.kdnuggets.com/
Li, J ., Dong, G., & R amamohanarao, K . (2001). Making Use of the Most Expressive Jumping Emerging Patterns for Classification.Knowledge and Information Systems, 3 (2), 131-145.
Spits, Warnars. 2014, Mining Frequent Pattern with Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP). 20142nd International Conference on Information and Communication Technology (ICoICT). Jurnal Vol. 978-1-4799-3580-2/14/$31.00 ©2014 IEEE.
Spits Warnars, (2014), “Mining Frequent and Similar Patterns with Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) Data Mining Technique”, International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), vol. 3, Issue 11, pp.266-276.
Tubagus M. Akhriza, Yinghua MA dan Jianhua LI. 2015, A Novel Fibonacci Windows Model for Finding Emerging Patterns over
Online Data Stream. 2015 International Conference on Cyber Security of Smart cities, Industrial Control System and Communications (SSIC). Jurnal Vol. 978-1-4673-7977-9/15/$31.00 ©2015 IEEE.
Turban, E., J.E. Aronson dan T.P. Liang. 2005. Decision Support System and Intelligent Systems - 7thed. Pearson Education, Inc. Pearson Education, Inc. Dwi Prabantini (penterjemah). 2005. Sistem Pendukung Keputusan dan Sistem Cerdas. Penerbit ANDI. Yogyakarta.
Refbacks
- There are currently no refbacks.
Jumlah Pengunjung :