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Analisis Sentimen Online Review Pengguna Bukalapak Menggunakan Metode Algoritma TF-IDF

JUSS (Jurnal Sains dan Sistem Informasi) · Vol. 2 No. 2 (2021) · hlm. 35–39

DOI
10.22437/juss.v2i2.8469
ISSN
2614-8277
Terbit
2021-11-05
Penerbit
Prodi Sistem Informasi FST Universitas Jambi
Bahasa
en

Abstrak

Bukalapak is one of the Customer-To Customer (C2C) e-commerce models. This model is the most widely applied and found on e-commerce sites in Indonesia. The Customer-To Customer (C2C) market is currently still dominant in Indonesia's online retail market. Data collected from Euromonitor estimates that the C2C market contributed 3% of the retail market in Indonesia in 2017, while the B2C market contributed 1.7%. One text mining analysis is that sentiment analysis can be applied to companies that issue a product or service and provide services to receive opinions (feedback) from consumers for the product. Sentiment analysis is applied to classify positive, negative, and neutral feedback from consumers so as to speed up and simplify the company's task to review their product deficiencies. The researcher conducted further analysis on Bukalapak user reviews to find out how user comments or opinions were on Bukalapak using the TF-IDF Algorithm method. And it can be concluded that based on customer review reviews in Bukalapak have a good rating or perception of this Vans shoe product. Can be seen from the results of Sentiments, Sentiment Visualization and WordCloud Visualization which shows that positive reviews have a higher frequency of 70%.

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  • Universitas Jambi
  • Universitas Gadjah Mada
  • Hasanuddin University
  • Institut Teknologi Sepuluh Nopember
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Edi SaputraUniversitas Jambi8
Dawam SuprayogiAfiliasi tidak diketahui7
Mauladi MauladiAfiliasi tidak diketahui6
Benedika Ferdian HutabaratUniversitas Jambi6
Novita SariAfiliasi tidak diketahui5
Daniel ArsaUniversitas Jambi5
Rizqa Raaiqa BintanaInstitut Teknologi Sepuluh Nopember3
Indra WeniUniversitas Jambi3
Dewi LestariHasanuddin University3
Reni AryaniAfiliasi tidak diketahui3
Zainil AbidinUniversitas Jambi2
Sigit IndrawijayaAfiliasi tidak diketahui2
Mutia Fadhila PutriAfiliasi tidak diketahui2
Tesa Ananda PutriUniversitas Jambi2
Notasonda Soni PutraUniversitas Jambi1
Andre Ilham SaputraAfiliasi tidak diketahui1
Shally YanovaAfiliasi tidak diketahui1
Azhari SnUniversitas Gadjah Mada1
Muksin AlfalahUniversitas Jambi1
Norman SyariefUniversitas Jambi1
Mutia Fadhila PutriAfiliasi tidak diketahui1
Indryani IndryaniAfiliasi tidak diketahui1
Akhmad Fikri RosyadiAfiliasi tidak diketahui1
Dedy SetiawanAfiliasi tidak diketahui1
Sylvia Kartika Wulan BhayangkariAfiliasi tidak diketahui1
Lucky Enggraini FitriAfiliasi tidak diketahui1
Rizqa Raaiqa BintanaAfiliasi tidak diketahui1
Teguh SumarsonoAfiliasi tidak diketahui1
Sugih SuhartiniAfiliasi tidak diketahui1
Dwi KurniawanAfiliasi tidak diketahui1
Yenny YuniartiAfiliasi tidak diketahui1
Mohamad IlhamiUniversitas Jambi1
SahrialAfiliasi tidak diketahui1
Tedjo SukmonoAfiliasi tidak diketahui1
Tia WulandariAfiliasi tidak diketahui1
Jodion SiburianAfiliasi 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://online-journal.unja.ac.id/index.php/JUSS/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">Analisis Sentimen Online Review Pengguna Bukalapak Menggunakan Metode Algoritma TF-IDF</dc:title>
	<dc:creator xml:lang="en">Utomo, Pradita Eko Prasetyo </dc:creator>
	<dc:creator xml:lang="en">Manaar, Manaar</dc:creator>
	<dc:creator xml:lang="en">Khaira, Ulfa </dc:creator>
	<dc:creator xml:lang="en"> Suratno, Tri</dc:creator>
	<dc:description xml:lang="en">Bukalapak is one of the Customer-To Customer (C2C) e-commerce models. This model is the most widely applied and found on e-commerce sites in Indonesia. The Customer-To Customer (C2C) market is currently still dominant in Indonesia's online retail market. Data collected from Euromonitor estimates that the C2C market contributed 3% of the retail market in Indonesia in 2017, while the B2C market contributed 1.7%. One text mining analysis is that sentiment analysis can be applied to companies that issue a product or service and provide services to receive opinions (feedback) from consumers for the product. Sentiment analysis is applied to classify positive, negative, and neutral feedback from consumers so as to speed up and simplify the company's task to review their product deficiencies. The researcher conducted further analysis on Bukalapak user reviews to find out how user comments or opinions were on Bukalapak using the TF-IDF Algorithm method. And it can be concluded that based on customer review reviews in Bukalapak have a good rating or perception of this Vans shoe product. Can be seen from the results of Sentiments, Sentiment Visualization and WordCloud Visualization which shows that positive reviews have a higher frequency of 70%.
&amp;nbsp;</dc:description>
	<dc:publisher xml:lang="en">Prodi Sistem Informasi FST Universitas Jambi</dc:publisher>
	<dc:date>2021-11-05</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://online-journal.unja.ac.id/JUSS/article/view/8469</dc:identifier>
	<dc:identifier>10.22437/juss.v2i2.8469</dc:identifier>
	<dc:source xml:lang="en">JUSS (Jurnal Sains dan Sistem Informasi); Vol. 2 No. 2 (2019): Jurnal Sains dan Sistem Informasi; 35-39</dc:source>
	<dc:source>2614-8277</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/JUSS/article/view/8469/9930</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2019 JUSS (Jurnal Sains dan Sistem Informasi)</dc:rights>
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