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Open access Jurnal SINTA 2

Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 3 (2019) · pp. 263–272

DOI
10.22146/ijccs.47046
ISSN
2460-7258, 1978-1520
Published
2019-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets.

Keywords

Collaboration network of the authors

40 authors · 70 co-authorship links · 7 across different organisations · showing the 40 most connected

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

  • Universitas Gadjah Mada
  • Syarif Hidayatullah State Islamic University Jakarta
  • Universitas Dian Nuswantoro
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Darmawan Lahru RiatmaAffiliation unknown1
Yusuf Fadhlilla RahmanAffiliation unknown1
Trisna Ari RoshintaAffiliation unknown1
Masbahah MasbahahAffiliation unknown1
Ahmad Faisal SaniAffiliation unknown1
Rifa KhoirunisaAffiliation unknown1
Anifudin AzisUniversitas Gadjah Mada1
Praretno WibowoAffiliation unknown1
Ghulam ImanuddinUniversitas Gadjah Mada1
Suci Karunia PrilistyaUniversitas Gadjah Mada1
Juwita MaharantiAffiliation unknown1
Iqnatius Dimas NugrohoUniversitas Gadjah Mada1
Amelia NursantiUniversitas Gadjah Mada1
Azhari SnUniversitas Gadjah Mada1
Winda YulitaUniversitas Gadjah Mada1
Tri Kuntoro PriyambodoUniversitas Gadjah Mada1
Zaied ShouranUniversitas Gadjah Mada1
Friska Ayu ListyaUniversitas Gadjah Mada1
Edi WinarkoUniversitas Gadjah Mada1
Sholikin Ady ChandraUniversitas Gadjah Mada1
Mhd. Theo Ari BangsaUniversitas Gadjah Mada1
Yohanes SuyantoUniversitas Gadjah Mada1
Wahyono WahyonoUniversitas Gadjah Mada1
Riska Amalia PraptiwiUniversitas Gadjah Mada1
Dayan Ramly RamadhanUniversitas Gadjah Mada1
Khabib MustofaUniversitas Gadjah Mada1
Amalia Nur AnggraeniUniversitas Gadjah Mada1
Hendri Kurniawan PrakosaUniversitas Gadjah Mada1
Huda MustakimUniversitas Gadjah Mada1
Dika Permana PutraUniversitas Gadjah Mada1
Anindhita NisitasariUniversitas Gadjah Mada1
Raymond LuisUniversitas Gadjah Mada1
Nur FadilahUniversitas Gadjah Mada1
Muthia Farah HanifaSyarif Hidayatullah State Islamic University Jakarta1
Anugrah Tri RamadhanUniversitas Dian Nuswantoro1
Ni’Matul HusnaUniversitas Gadjah Mada1
Nabila Apriliana WidiyonoUniversitas Gadjah Mada1

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">Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes</dc:title>
	<dc:creator>Haqimi, Nur Azizul</dc:creator>
	<dc:creator>Rokhman, Nur</dc:creator>
	<dc:creator>Priyanta, Sigit</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Instagram; Spam; Complementary Naïve Bayes; Support Vector Machine</dc:subject>
	<dc:description xml:lang="en-US">Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2019-07-31</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/47046</dc:identifier>
	<dc:identifier>10.22146/ijccs.47046</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 3 (2019): July; 263-272</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/47046/25085</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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