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Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 4 (2019) · pp. 399–408

DOI
10.22146/ijccs.50574
ISSN
2460-7258, 1978-1520
Published
2019-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Instagram is a social media that has the potential to be used to increase awareness of a product. Approximately 70% of users spend their time searching for a product on Instagram. Many people promote their products with a lack of attention to the target. So that not infrequently the information distributed is inaccurate information and not following user characteristics. This study aims to cluster the characteristics of Instagram users based on hashtag compatibility. The method used in this study is the K-Means Clustering method. Based on the results of the experiment, this research succeeded in clustering Instagram users based on the hashtag match on the text caption. Besides, TF-IDF can be used as a feature suitable for the K-Means Klastering method. The results of the hashtag "#kopi" analysis resulted in hashtag suggestions that can be used for the promotion of a product related to coffee, including the hashtag #coffeeshop and #coffee with total usage of 14968 captions.

Keywords

Collaboration network of the authors

4 authors · 4 co-authorship links · 1 across different organisations

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  • Universitas Jenderal Achmad Yani Yogyakarta
  • Universitas Gadjah Mada
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Adri PriadanaUniversitas Gadjah Mada1
Muhammad Rifqi Ma'arifAffiliation 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">Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform</dc:title>
	<dc:creator>Habibi, Muhammad</dc:creator>
	<dc:creator>Cahyo, Puji Winar</dc:creator>
	<dc:subject xml:lang="en-US">Data Mining; Text Mining; Social Media Analytic</dc:subject>
	<dc:subject xml:lang="en-US">Clustering; Instagram; K-Means; Social Media; Text Analysis</dc:subject>
	<dc:description xml:lang="en-US">Instagram is a social media that has the potential to be used to increase awareness of a product. Approximately 70% of users spend their time searching for a product on Instagram. Many people promote their products with a lack of attention to the target. So that not infrequently the information distributed is inaccurate information and not following user characteristics. This study aims to cluster the characteristics of Instagram users based on hashtag compatibility. The method used in this study is the K-Means Clustering method. Based on the results of the experiment, this research succeeded in clustering Instagram users based on the hashtag match on the text caption. Besides, TF-IDF can be used as a feature suitable for the K-Means Klastering method. The results of the hashtag "#kopi" analysis resulted in hashtag suggestions that can be used for the promotion of a product related to coffee, including the hashtag #coffeeshop and #coffee with total usage of 14968 captions.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Directorate of Research and Community Service Directorate General of Research and Development Strengthening (DRPM) Ministry of Research and Higher Education (Kemristekdikti) of the Republic of Indonesia</dc:contributor>
	<dc:date>2019-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/50574</dc:identifier>
	<dc:identifier>10.22146/ijccs.50574</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 4 (2019): October; 399-408</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/50574/26047</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 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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