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Open access Jurnal SINTA 2

Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 1 No. 2 (2007)

DOI
10.22146/ijccs.2278
ISSN
2460-7258, 1978-1520
Published
2007-06-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

AbstractCapital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting.} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM) in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern.Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition

Keywords

Collaboration network of the authors

12 authors · 14 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Multimedia Nusantara University
  • Institut Bisnis dan Teknologi Indonesia
  • Sunan Kalijaga State Islamic University Yogyakarta
  • Udayana University
  • State University of Medan
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Seng HansunMultimedia Nusantara University1
Elena YustinaUniversitas Gadjah Mada1
Desak Made Dwi Utami PutraInstitut Bisnis dan Teknologi Indonesia1
Shofwatul UyunSunan Kalijaga State Islamic University Yogyakarta1
I Wayan SuprianaUdayana University1
Siska Dewi LestariAffiliation unknown1
Zulfahmi IndraState University of Medan1
Retantyo WardoyoUniversitas Gadjah Mada1
Sri T. HartatiUniversitas Gadjah Mada1
Azhari AzhariAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern</dc:title>
	<dc:creator>Annatje Sumarauw, Sylvia Jane</dc:creator>
	<dc:creator>Subanar, Subanar</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Data Mining; Fuzzy c-Means Clustering Model; Pattern Recognition</dc:subject>
	<dc:description xml:lang="en-US">AbstractCapital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM) in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern.Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">indoceiss</dc:contributor>
	<dc:date>2007-06-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/2278</dc:identifier>
	<dc:identifier>10.22146/ijccs.2278</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 1, No 2 (2007): July</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/2278/2038</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2007 IJCCS - Indonesian Journal of Computing and Cybernetics Systems</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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