Pendekatan Algoritma Naïve Bayes dan Support Vector Machine dalam Menganalisis Tanggapan Terhadap Penutupan Tik Tok Shop

Fandi kurniawan; Qois Al Qorni

doi:10.33557/jurnalmatrik.v25i3.2732

Fandi kurniawan Universitas Muhammadiyah Kota Metro
Qois Al Qorni Universitas Multi Data Palembang

DOI: https://doi.org/10.33557/jurnalmatrik.v25i3.2732

Keywords: Analysis, Naive Bayes, SVM, Tiktok

Abstract

The development of information technology, especially in social media such as TikTok, has had a significant impact on people's shopping behavior patterns. Sentiment analysis is aimed at understanding general views regarding the closure of the TikTok Shop, describing positive or negative opinions. The algorithms used are Naïve Bayes and Support Vector Machine (SVM) to analyze responses to the closure of the TikTok Shop. The research methodology involves the process of crawling the Twitter dataset with the keyword "Tik Tok Shop", data cleaning, sentiment labeling, model building, and performance testing. The results of the sentiment analysis are parsed and analyzed to describe the general sentiment towards the closure of the Tik Tok Shop. The conclusion results show that the Naïve Bayes algorithm achieved an accuracy level of 97.91%, with positive recall of 100%, positive precision of 96.30%, negative recall of 95.42%, and negative precision of 100%. Meanwhile the SVM algorithm achieved an accuracy of 91.29%, with a positive recall of 100%, positive precision of 86.19%, negative recall of 80.92%, and negative precision of 100%. Overall, Naïve Bayes provides the highest accuracy results of 97.91%, while SVM provides an accuracy of 91.29%.

References

M. R. Sholihin, W. Arianto, D. F. Khasanah, S. Widya, and G. Lumajang, “Prosiding 4th Seminar Nasional dan Call for Papers Fakultas Ekonomi Universitas Muhammadiyah Jember Hal.”

D. Deriyanto, F. Qorib, J. I. Komunikasi, U. Tribhuwana, and T. Malang, “Persepsi Mahasiswa Universitas Tribhuwana Tunggadewi Malang Terhadap Penggunaan Aplikasi Tik Tok,” 2018. [Online]. Available: www.publikasi.unitri.ac.id

B. Liu and L. Zhang, “A survey of opinion mining and sentiment analysis,” in Mining Text Data, vol. 9781461432234, Springer US, 2012, pp. 415–463. doi: 10.1007/978-1-4614-3223-4_13.

E. Kontopoulos, C. Berberidis, T. Dergiades, and N. Bassiliades, “Ontology-based sentiment analysis of twitter posts,” Expert Syst Appl, vol. 40, no. 10, pp. 4065–4074, Aug. 2013, doi: 10.1016/j.eswa.2013.01.001.

C. L. A. Clarke, N. Fuhr, N. Kando, W. Kraaij, and A. P. De Vries, SIGIR ’07 : 30th annual International ACM SIGIR Conference on Research and Development in Information Retrieval : July 23-27, 2007, Amsterdam, the Netherlands.

T. Dergiades, “Do investors’ sentiment dynamics affect stock returns? Evidence from the US economy,” Econ Lett, vol. 116, no. 3, pp. 404–407, Sep. 2012, doi: 10.1016/j.econlet.2012.04.018.

A. Deolika and E. Taufiq Luthfi, “Analisis Pembobotan Kata Pada Klasifikasi Text Mining,” Jurnal Teknologi Informasi, vol. 3, no. 2, 2019.

A. Felicia Watratan, A. B. Puspita, D. Moeis, S. Informasi, and S. Profesional Makassar, “Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia,” 2020. [Online]. Available: http://journal.isas.or.id/index.php/JACOST

T. Joachims, “Text Categorization with Support Vector Machines: Learning with Many Relevant Features.”

Y. Yang and X. Liu, “A re-examination of text categorization methods,” 1999. [Online]. Available: www.cs.cmu.edu/yiming/

M. F. Rahman, M. Ilham Darmawidjadja, and D. Alamsah, “Klasifikasi Untuk Diagnosa Diabetes Menggunakan Metode Bayesian Regularization Neural Network (RBNN),” 2017.