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Application of the Naïve Bayes Classifier Algorithm using N-Gram and Information Gain to Improve the Accuracy of Restaurant Review Sentiment Analysis

Journal of Advances in Information Systems and Technology · Vol. 2 No. 2 (2020) · hlm. 11–20

DOI
10.15294/jaist.v2i2.44303
ISSN
2715-999X, 2714-9714
Terbit
2020-10-30
Penerbit
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

A consumer's review is an essential aspect for influencing others in determining decisions. The process of identifying positive or negative reviews can be conducted through sentiment analysis. One of the popular techniques in the sentiment analysis is the Naïve Bayes Classifier (NBC) algorithm, which has optimal performance. The purpose of this study was to improve the accuracy of the classifier in the analysis of restaurant review sentiments by applying N-Gram as feature extraction and Information Gain as a feature selection. N-Gram is used to produce new features that are more varied, while information gain functions to select relevant features with high weights. The dataset used in this study is the sentiment labeled dataset from UCI machine learning. The results of applying the NBC have an accuracy of 82.5%. The research results revealed that the Naïve Bayes Classifier's accuracy by using N-Gram and information gain of 86%. The application of N-Gram and information gain in the NBC algorithm can be concluded that it has succeeded in improving the classification accuracy of the restaurant review sentiment analysis with an increase in accuracy of 3.5%.

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  • State University of Semarang
  • Institut Teknologi Kalimantan
  • Tun Hussein Onn University of Malaysia
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Riza ArifudinState University of Semarang5
Alamsyah AlamsyahInstitut Teknologi Kalimantan3
Amin SuyitnoAfiliasi tidak diketahui3
Kholiq BudimanAfiliasi tidak diketahui2
Untsa Zaaidatunni'mahAfiliasi tidak diketahui2
Nuril AsyrofiyyahAfiliasi tidak diketahui2
Dany PradanaAfiliasi tidak diketahui2
Dwijanto DwijantoAfiliasi tidak diketahui2
Budi PrasetiyoState University of Semarang1
Much Aziz MuslimTun Hussein Onn University of Malaysia1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Anggyi Trisnawan PutraAfiliasi tidak diketahui1
zaenal abidinAfiliasi tidak diketahui1
Yahya Nur IfrizaState University of Semarang1
Tiffany Ovilia Dwi LestariAfiliasi tidak diketahui1
Chairun NisakAfiliasi tidak diketahui1
Kevin Tito HutahaeanAfiliasi tidak diketahui1
Defa RiandinaAfiliasi tidak diketahui1
Isa AkhlisState University of Semarang1
Taufik Fajar MubaroqAfiliasi tidak diketahui1
Rahmalia NuridaAfiliasi tidak diketahui1
Safit FirmansyahAfiliasi tidak diketahui1
Anita Ayu SeptiantinaAfiliasi tidak diketahui1
Arief Broto SusiloAfiliasi tidak diketahui1
Tomi Bagus NugrohoAfiliasi tidak diketahui1
Wahyu SyafrizalAfiliasi tidak diketahui1
Oktaria Gina KhoirunnisaAfiliasi tidak diketahui1
Yusuf AffandiAfiliasi tidak diketahui1
Erika Noor DiantiAfiliasi tidak diketahui1
Akbar Lintang AjiAfiliasi tidak diketahui1
Fabian Dominggus Eka BoroAfiliasi tidak diketahui1
Muhammad AfifudinAfiliasi tidak diketahui1
Ahmad Ramadhan AuniAfiliasi tidak diketahui1
Muhammad KharisAfiliasi tidak diketahui1
Fajar Eska PradhanaAfiliasi tidak diketahui1
Ratnaningtyas Widyani PurnamasariAfiliasi tidak diketahui1
Firar Anitya SariAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/jaist/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Application of the Naïve Bayes Classifier Algorithm using N-Gram and Information Gain to Improve the Accuracy of Restaurant Review Sentiment Analysis</dc:title>
	<dc:creator>Solikhatun, Apriani</dc:creator>
	<dc:creator>Sugiharti, Endang</dc:creator>
	<dc:description xml:lang="en-US">A consumer's review is an essential aspect for influencing others in determining decisions. The process of identifying positive or negative reviews can be conducted through sentiment analysis. One of the popular techniques in the sentiment analysis is the Naïve Bayes Classifier (NBC) algorithm, which has optimal performance. The purpose of this study was to improve the accuracy of the classifier in the analysis of restaurant review sentiments by applying N-Gram as feature extraction and Information Gain as a feature selection. N-Gram is used to produce new features that are more varied, while information gain functions to select relevant features with high weights. The dataset used in this study is the sentiment labeled dataset from UCI machine learning. The results of applying the NBC have an accuracy of 82.5%. The research results revealed that the Naïve Bayes Classifier's accuracy by using N-Gram and information gain of 86%. The application of N-Gram and information gain in the NBC algorithm can be concluded that it has succeeded in improving the classification accuracy of the restaurant review sentiment analysis with an increase in accuracy of 3.5%.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang</dc:publisher>
	<dc:date>2020-10-30</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/jaist/article/view/44303</dc:identifier>
	<dc:identifier>10.15294/jaist.v2i2.44303</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Advances in Information Systems and Technology; Vol 2 No 2 (2020): October; 11-20</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:source>10.15294/jaist.v2i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/jaist/article/view/44303/18042</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Journal of Advances in Information Systems and Technology</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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