Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

Comparison of Filter and Wrapper Based Feature Selection Methods on Spam Comment Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 3 (2021) · pp. 245–254

DOI
10.22146/ijccs.66965
ISSN
2460-7258, 1978-1520
Published
2021-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The continuous growth of the internet has led to the use of social media for various purposes increase. For instance, some irresponsible parties take advantage of the comment feature on social media platforms to harm others by providing spam comments on the shared object. Furthermore, variation of comments creates many features to be processed, thereby negatively impacting the performance of a classification algorithm. Therefore, this study aims to solve the problem associated with spam comments by comparing filter and wrapper based feature selection using text classification techniques. Data collected from training and test data of 4944 and 100 comments showed that the best accuracy, precision, recall, and f-measure of MNB are 96%, 100%, 92%, and 95.8%. The best accuracy is achieved using feature selection by combining Chi-Square and Sequential Forward Selection methods with a subset of 500 features. Furthermore, the accuracy increase in the MNB and SVM classifications are 8% and 4%. This research concludes that the combination of feature selection improves the classification performance of Indonesian language spam comments.

Keywords

Collaboration network of the authors

37 authors · 62 co-authorship links · 21 across different organisations

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

  • Universitas Gadjah Mada
  • Universitas Stikubank
  • Universitas Trunodjoyo Madura
  • Sekolah Tinggi Multi Media "MMTC" Yogyakarta
  • Universitas Teknokrat Indonesia
  • Syarif Hidayatullah State Islamic University Jakarta
  • Universitas Dian Nuswantoro
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Edi WinarkoUniversitas Gadjah Mada5
Retantyo WardoyoUniversitas Gadjah Mada4
Edhy SutantaUniversitas Gadjah Mada4
Wiwien HadikurniawatiUniversitas Stikubank2
Ghulam ImanuddinUniversitas Gadjah Mada1
Suci Karunia PrilistyaUniversitas Gadjah Mada1
Oesman Hendra KelanaUniversitas Gadjah Mada1
Anie Rose IrawatiUniversitas Gadjah Mada1
Wikranta ArsaUniversitas Gadjah Mada1
Firmansyah AdiputraUniversitas Trunodjoyo Madura1
Erma SusantiUniversitas Gadjah Mada1
Teguh SusyantoAffiliation unknown1
David KristiadiSekolah Tinggi Multi Media "MMTC" Yogyakarta1
Sunu Pinasthika FajarUniversitas Gadjah Mada1
Azhari SnUniversitas Gadjah Mada1
Winda YulitaUniversitas Gadjah Mada1
Styawati StyawatiUniversitas Teknokrat Indonesia1
Nur Azizul HaqimiUniversitas Gadjah Mada1
Nur RokhmanUniversitas Gadjah Mada1
Sholikin Ady ChandraUniversitas Gadjah Mada1
Mhd. Theo Ari BangsaUniversitas Gadjah Mada1
Yohanes SuyantoUniversitas Gadjah Mada1
Shona Chayy BilqisthUniversitas Gadjah Mada1
Huda MustakimUniversitas Gadjah Mada1
Dika Permana PutraUniversitas 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
Rhamdan Syahrul MubarakUniversitas Gadjah Mada1
Adisti Anjani PutriUdayana University1
Tenia WahyuningrumInstitut Teknologi Telkom Purwokerto1
Ridowati GunawanUniversitas 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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Comparison of Filter and Wrapper Based Feature Selection Methods on Spam Comment Classification</dc:title>
	<dc:creator>Anggraeni, Amalia Nur</dc:creator>
	<dc:creator>Mustofa, Khabib</dc:creator>
	<dc:creator>Priyanta, Sigit</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Comment Spam; Feature Selection; Naïve Bayes; Support Vector Machine; Text Classification</dc:subject>
	<dc:description xml:lang="en-US">The continuous growth of the internet has led to the use of social media for various purposes increase. For instance, some irresponsible parties take advantage of the comment feature on social media platforms to harm others by providing spam comments on the shared object. Furthermore, variation of comments creates many features to be processed, thereby negatively impacting the performance of a classification algorithm. Therefore, this study aims to solve the problem associated with spam comments by comparing filter and wrapper based feature selection using text classification techniques. Data collected from training and test data of 4944 and 100 comments showed that the best accuracy, precision, recall, and f-measure of MNB are 96%, 100%, 92%, and 95.8%. The best accuracy is achieved using feature selection by combining Chi-Square and Sequential Forward Selection methods with a subset of 500 features. Furthermore, the accuracy increase in the MNB and SVM classifications are 8% and 4%. This research concludes that the combination of feature selection improves the classification performance of Indonesian language spam comments.</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>2021-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/66965</dc:identifier>
	<dc:identifier>10.22146/ijccs.66965</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 3 (2021): July; 245-254</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/66965/31838</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 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>
</oai_dc:dc>