Analisis Sentimen Perbedaan Pendapat Netizen Indonesia Terhadap Penutupan Tiktok Shop Menggunakan Algoritma Naïve Bayes

 Eko Kurnianto (Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia)
 (*)Dimas Febriawan Mail (Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: December 15, 2023; Published: December 29, 2023

Abstract

This research uses the Naïve Bayes algorithm to analyze the sentiments of Indonesian netizens regarding the closure of the TikTok Shop. This research focuses on analyzing differences of opinion spread on social media platforms. Data obtained from social media such as Youtube, Tiktok, and Threads. There is data that will later be used in this research with a total of 1366 data. Then, there were 987 positive data and 379 negative data. After conducting research, results will be obtained with an accuracy of 86.97% in the first experiment which does not use the Split Data operator, and an accuracy of 89.23% in the second experiment which uses the Split Data operator. Then the results of this analysis reveal significant variations in sentiment among Indonesian netizens regarding the closure of the TikTok Shop. Some groups of netizens may express disappointment or disapproval while others may show support for the decision. The analysis also identified key factors influencing dissent, such as user experience, expectations of the platform and economic impact. Due to this, this research contributes to the field of sentiment analysis and natural language processing which applies splitting procedures so that netizen comment data on the platform can be classified.

Keywords


Sentiment Analysis; Naïve Bayes algorithm; TikTok Shop; Fast Miner; Identification

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References

A. S. Widagdo and A. N. W., Bambang Soedijono, “Analisis Tingkat Kepopuleran E-Commerce Di Indonesia Berdasarkan Sentimen Sosial Media Menggunakan Metode Naïve Bayes,” J. Inf. Politek. Indonusa Surakarta, vol. 6, 2020.

S. Sari, U. Khaira, P. Pradita, and T. S. Tri, “… Beauty Shaming Di Media Sosial Twitter Menggunakan Algoritma SentiStrength: Sentiment Analysis Against Beauty Shaming Comments on Twitter Social Media …,” Indones. J. …, vol. 1, no. 1, pp. 71–78, 2021, [Online]. Available: https://journal.irpi.or.id/index.php/ijirse/article/view/55%0Ahttps://journal.irpi.or.id/index.php/ijirse/article/download/55/24.

J. Mantik et al., “Application Of N-Gram On K-Nearest Neighbor Algorithm To Sentiment Analysis Of TikTok Shop Shopping Features,” J. Mantik, vol. 6, no. 3, pp. 2685–4236, 2022.

C. Maulida, T. Yunanda, M. Hanafi, W. Mega, and P. Dhuhita, “Sentiment Analysis on TikTok Shop Reviews Using Long Short-Term Memory Method to Find Business Opportunity,” Inf. J. Ilm. Bid. Teknol. Inf. dan Komun., vol. 9, no. 1, pp. 1–7, 2024, [Online]. Available: https://doi.org/10.25139/inform.v9i1.6524.

D. Ardiansyah, A. Saepudin, R. Aryanti, E. Fitriani, and Royadi, “Analisis Sentimen Review Pada Aplikasi Media Sosial Tiktok Menggunakan Algoritma K-Nn Dan Svm Berbasis Pso,” J. Inform. Kaputama, vol. 7, no. 2, pp. 233–241, 2023, doi: 10.59697/jik.v7i2.148.

D. Oktaheriyani, M. A. Wafa, and S. Shadiqien, “ANALISIS PERILAKU KOMUNIKASI PENGGUNA MEDIA SOSIAL TIKTOK (Studi Pada Mahasiswa Fakultas Ilmu Sosial dan Ilmu Politik UNISKA MAB Banjarmasin),” ePRINTS UNISKA , pp. 1–62, 2020, [Online]. Available: http://eprints.uniska-bjm.ac.id/id/eprint/3504.

A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 785–795, 2022, doi: 10.35957/jatisi.v9i2.1835.

S. C. Nandaresta and C. Warman, “Terhadap Tiktok Shop Dan Shopee Di Twitter Menggunakan Metode Naïve Bayes Dan Knn ( K- Nearest Neighbor,” Sismatik, vol. 12, no. 1, pp. 1–9, 2023.

S. Jafar and A. Nur, “Analysis Of Twitter User Sentiment To Tiktok Shop Using Naïve Bayes And Decision Tree Algorithms,” pp. 8–14, 2010.

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021, [Online]. Available: https://ejurnal.tunasbangsa.ac.id/index.php/jsakti/article/view/369/348.

A. Surahman, A. F. Octaviansyah, and D. Darwis, “Ekstraksi Data Produk E-Marketplace Sebagai Strategi Pengolahan Segmentasi Pasar Menggunakan Web Crawler,” Sistemasi, vol. 9, no. 1, p. 73, 2020, doi: 10.32520/stmsi.v9i1.580.

A. Aziz, “Analisis Sentimen Identifikasi Opini Terhadap Produk, Layanan dan Kebijakan Perusahaan Menggunakan Algoritma TF-IDF dan SentiStrength,” J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 1, p. 115, 2022.

A. D. Adhi Putra, “Analisis Sentimen pada Ulasan pengguna Aplikasi Bibit Dan Bareksa dengan Algoritma KNN,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 2, pp. 636–646, 2021, doi: 10.35957/jatisi.v8i2.962.

S. Lestari and S. Saepudin, “Analisis Sentimen Vaksin Sinovac Pada Twitter Menggunakan Algoritma Naive Bayes,” SISMATIK (Seminar Nas. Sist. Inf. dan Manaj. Inform., pp. 163–170, 2021.

Dedi Darwis, Nery Siskawati, and Zaenal Abidin, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” J. TEKNO KOMPAK, vol. 15, no. 1, pp. 131–145, 2020.

H. F. Putro, R. T. Vulandari, and W. L. Y. Saptomo, “Penerapan Metode Naive Bayes Untuk Klasifikasi Pelanggan,” J. Teknol. Inf. dan Komun., vol. 8, no. 2, 2020, doi: 10.30646/tikomsin.v8i2.500.

E. Fitri, “Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine,” J. Transform., vol. 18, no. 1, p. 71, 2020, doi: 10.26623/transformatika.v18i1.2317.

F. Sidik, I. Suhada, A. H. Anwar, and F. N. Hasan, “Analisis Sentimen Terhadap Pembelajaran Daring Dengan Algoritma Naive Bayes Classifier,” J. Linguist. Komputasional, vol. 5, no. 1, p. 34, 2022, doi: 10.26418/jlk.v5i1.79.

T. Wiratama Putra, A. Triayudi, and A. Andrianingsih, “Analisis Sentimen Pembelajaran Daring Menggunakan Metode Naïve Bayes, KNN, dan Decision Tree,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 6, no. 1, pp. 20–26, 2022, doi: 10.35870/jtik.v6i1.368.

Y. R. Pusvitasari, “Analisis Penggunaan Fitur TikTok Shop Pada Perilaku Konsumtif Mahasiswa Fakultas Dakwan IAIN Salatiga,” p. 1, 2022, [Online]. Available: http://e-repository.perpus.iainsalatiga.ac.id/14705/.

RAVI SHAH, Guide to Tokenizers and Preprocessing. 2022.

Konrad Banachewicz and Luca Massaron, Kaggle Workbook. Packt Publishing, Limited, Packt Publishing, 2022.

miftahul kahfi al Fath, “Analisis Sentimen Komentar Kebijakan Full Day School Dari Facebook Page Kemendikbud Ri Menggunakan Algoritma Naïve Bayes Classifier,” Tek. Inform., p. 169, 2018, [Online]. Available: https://repository.uinjkt.ac.id/dspace/handle/123456789/47898.

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