PRINCIPAL COMPONENT ANALYSIS UNTUK ANALISA POLA TANGKAPAN IKAN DI INDONESIA

  • Titin Kristiana Manajemen Informatika AMIK BSI Jakarta
Keywords: Penangkapan Ikan, Algoritma Apriori, Association rule, principal component analysis

Abstract

Different kinds of fish in Indonesia is very much known to exist more than 80 species of fish caught in the waters of Indonesia. To find out which type of fish caught necessary analysis of the data pattern catches so as to know what kind of fish are caught. Search pattern or associative relationships of large-scale data that are closely related to data mining. Analysis of the association or the association rule mining is a data mining technique to discover the rules of associative between a combination of items. In the association rule method, there are two processes, namely the process of generating Frequent Itemset and trenching association rules. Frequent Itemset Generation is a process to get itemset interconnected and has a value of association based on the value of support and confidence. The algorithm used to generate the frequent itemset is Apriori Algorithm. Apriori algorithm has a weakness in the appropriate feature extraction that is used to attribute causing rule that formed a research a lot in based applying apriori algorithm principal component analysis to obtain a more optimal rule. After experiments using apriori algorithm with a magnitude Φ = 30, min Support 80% and 80% Confidence min rule formed results totaled 82 rules. While the second experiment was done by using an algorithm based on principal component analysis prior the magnitude Φ = 30, min Support 80% and 80% Confidence min formed results amounted to 12 rules to fully lift the ratio of 1

References

Large Scale Data by Combining Class Association Rule Mining and Information : a Hybrid Approach. Internetworking Indonesian Journal , 17.

Balamurugan, P. r. (2009). Feature Selection for Large Scale Rule Mining and Information Gain : a Hybrid Approach . Internetworking Indonesia Journal , 17-24.

Berndtsson, M., Olsson, J. H., & Lundell, B.(2008). Thesis Projects A Guide for Students in Computer Science and Information Systems. Verlag London: Springer.

Dawson, C. W. (2009). Projects in Computing and Information System A Student's Guide. England: Addison-Wesley.

Gray, D. E. (2004). Doing Research in the Real World. New Delhi: SAGE.

Han, J., & Kamber, M. (2007). Data Mining Consepts and technique. San Fransisco: Morgan Kaufmann.

Hanash, C. C. (2003). Mining gene expresion databases for association rule. Bioinformatics , 79-86.

K, G. S., & Deepa, D. S. (2011). Analysis of Computing Algorithm using Momentum in Neural Networks.

Journal of computing, volume 3, issue 6 , 163-166.

Kothari, C. R. (2004). Research Methology methodes and Technique. India: New Age Interntional.

Kusrini, E. t. (2009). Algoritma Data Mining. Yogjakarta: Andi.

Larose, D. T. (2007). Data Mining Methods And Models. New Jersey: A John Wiley & Sons.

Maimon, O. (2010). Data Mining and Knowledge Discovery Handbook. New York Dordrecht Heldelberg London: Springer.

PUSDATIN. (2011). Kelautan dan Perikanan dalam angka 2010. Jakarta: Kementerian kelautan dan Perikanan.

Santosa, B. (2007). Data Mining Teknik Pemanfaatan Data Untuk keperluan Bisnis. Yogjakarta: Graha Ilmu.

Siombo, M. r. (2010). Hukum perikanan nasional dan internasional. Jakarta: Gramedia Pustaka Utama.

Tyas, E. W. (2008). Penerapan Association rule dengan menggunakan algoritma apriori untuk analisa pola data hasil tangkapan ikan. Konferensi dan temu Teknologi Informasi dan Komunikasi Untuk Infonesia .

Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining Practical Machine Learning Tools and Techniques Third Edition. USA: Morgan Kaufmann.

Yaya Mulyana, A. D. (2008). Konservasi kawasan perairan indonesia bagi masa depan dunia. Jakarta: Direktorat Konservasi dan Taman Nasional laut, Kementerian Kelautan dan Perikanan.
Published
2013-03-15
How to Cite
Kristiana, T. (2013). PRINCIPAL COMPONENT ANALYSIS UNTUK ANALISA POLA TANGKAPAN IKAN DI INDONESIA. Jurnal Techno Nusa Mandiri, 10(1), 78-87. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/562
Issue
Vol 10 No 1 (2013): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Maret 2013
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Articles
Copyright (c) 2013 Titin Kristiana

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