Melola Discovery

← Melola Discovery

Open access Journal SINTA 3

SENTIMENT ANALYSIS OF MERDEKA BELAJAR KAMPUS MERDEKA POLICY USING SUPPORT VECTOR MACHINE WITH WORD2VEC

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 1 (2023) · pp. 0481–0486

DOI
10.30598/barekengvol17iss1pp0481-0486
ISSN
2615-3017, 1978-7227
Published
2023-04-20
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Sentiment analysis is a data text analysis that classifies data into positive and negative sentiments. This study aims to obtain the results of sentiment classification related to Merdeka Belajar Kampus Merdeka policy on Twitter using support vector machine algorithm with Word2Vec feature extraction. Support Vector Machine is a classification algorithm that separates data classes using the optimum hyperplane. Text data used in sentiment analysis must change its numerical form by performing feature extraction. In this study, the feature extraction used is Word2Vec which represents words in vector form. Data in this study are tweets with the keyword "Kampus Merdeka" uploaded on Twitter as many as 10000 tweets. After preprocessing text data, data used to analyze sentiment was 1579 tweets. Sentiment classification resulted in classification model accuracy 89.87%, precision 91.20%, recall 84.44% and F-Measure 87.68%. Classification sentiment using support vector machine with Word2Vec feature extraction in this study produces a good model.

Collaboration network of the authors

40 authors · 117 co-authorship links · 5 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.

  • Padjadjaran University
  • Sekolah Tinggi Agama Islam Syubbanul Wathon Magelang
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Anisa KalondengAffiliation unknown4
Anna IslamiyatiAffiliation unknown4
Sitti SahrimanAffiliation unknown2
Andi Isna YunitaAffiliation unknown2
Andi M Alfin BasoAffiliation unknown2
M. Zaky Hisyam GozhiAffiliation unknown2
Vika Yugi KurniawanAffiliation unknown2
Nurtiti SunusiAffiliation unknown2
Ade Safira SeptianyAffiliation unknown2
Raupong RaupongAffiliation unknown2
Surya GumilarAffiliation unknown2
Muhamad SholikhunSekolah Tinggi Agama Islam Syubbanul Wathon Magelang1
Zulfatus SorayaAffiliation unknown1
Milna WafirahSekolah Tinggi Agama Islam Syubbanul Wathon Magelang1
Budi AyuningsihPadjadjaran University1
Iman HernamanPadjadjaran University1
Tidi DhalikaPadjadjaran University1
Destu Putra RosmawanAffiliation unknown1
Dedi RahmatPadjadjaran University1
Ansharullah AnsharullahUniversitas Halu Oleo1
Abdu Rahman BacoAffiliation unknown1
Alfiana AntohAffiliation unknown1
Yundy HafizriandaAffiliation unknown1
Ainun Utari BudistiharahAffiliation unknown1
Mamik UtamiAffiliation unknown1
Hajratul Ashwad KAffiliation unknown1
Afifah Mutiah NurdinAffiliation unknown1
Muh. Indirwan ArfanAffiliation unknown1
Muhammad FadilAffiliation unknown1
A.Muthiah Nur AngrianyAffiliation unknown1
Rini JamalAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation unknown1
Nirwan IlyasAffiliation unknown1
Haksar HaksarAffiliation unknown1
Nurhaliza RaisAffiliation unknown1
Maryana MaharaniAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 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">SENTIMENT ANALYSIS OF MERDEKA BELAJAR KAMPUS MERDEKA POLICY USING SUPPORT VECTOR MACHINE WITH WORD2VEC</dc:title>
	<dc:creator>Rezki, Nurul</dc:creator>
	<dc:creator>Thamrin, Sri Astuti</dc:creator>
	<dc:creator>Siswanto, Siswanto</dc:creator>
	<dc:description xml:lang="en-US">Sentiment analysis is a data text analysis that classifies data into positive and negative sentiments. This study aims to obtain the results of sentiment classification related to Merdeka Belajar Kampus Merdeka policy on Twitter using support vector machine algorithm with Word2Vec feature extraction. Support Vector Machine is a classification algorithm that separates data classes using the optimum hyperplane. Text data used in sentiment analysis must change its numerical form by performing feature extraction. In this study, the feature extraction used is Word2Vec which represents words in vector form. Data in this study are tweets with the keyword "Kampus Merdeka" uploaded on Twitter as many as 10000 tweets. After preprocessing text data, data used to analyze sentiment was 1579 tweets. Sentiment classification resulted in classification model accuracy 89.87%, precision 91.20%, recall 84.44% and F-Measure 87.68%. Classification sentiment using support vector machine with Word2Vec feature extraction in this study produces a good model.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-04-20</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7561</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss1pp0481-0486</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications; 0481-0486</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss1year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7561/5566</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Nurul Rezki, Sri Astuti Thamrin, Siswanto Siswanto</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>