ANALISIS SENTIMEN PENGGUNA TIK TOK SHOP MENGGUNAKAN ALGORITMA SVM
DOI:
https://doi.org/10.31949/jensitec.v10i01.7167Abstract
In today's digital landscape, social media platforms like TikTok Shop have gained immense popularity as online shopping hubs. This study advocates the utilization of Support Vector Machine (SVM) methodology to assess customer sentiment concerning products available on TikTok Shop. The primary objective is to assess SVM's efficacy in categorizing customer sentiments (positive, negative) based on their responses to product reviews. The research findings revealed a classification accuracy of 78.66%, precision of 78.01%, recall of 99.09%, and an F1 score of 87.30%. While these results showcase a commendable performance, the study recognizes room for improvement. Consequently, further enhancements are recommended, particularly in refining preprocessing techniques for both data and modeling. These additional preprocessing steps are anticipated to significantly enhance sentiment classification accuracy, ensuring more reliable outcomes and providing deeper insights into customer preferences within the realm of online shopping
Keywords:
Molting crab classification, Image Processing, Machine Learning, KNN, RFC, and SVM.Downloads
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.”
Tata Sutabri, “Analisis Sistem Informasi - Google Books,” Cv Andi Offset, 2012.
T. Sutabri, “Konsep Sistem Informasi - Tata Sutabri - Google Books,” Penerbit Andi. 2012.
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.
A. R. Isnain, A. I. Sakti, D. Alita, and N. S. Marga, “SENTIMEN ANALISIS PUBLIK TERHADAP KEBIJAKAN LOCKDOWN PEMERINTAH JAKARTA MENGGUNAKAN ALGORITMA SVM,” Jurnal Data Mining dan Sistem Informasi, vol. 2, no. 1, 2021, doi: 10.33365/jdmsi.v2i1.1021.
D. Alita, Y. Fernando, and H. Sulistiani, “IMPLEMENTASI ALGORITMA MULTICLASS SVM PADA OPINI PUBLIK BERBAHASA INDONESIA DI TWITTER,” Jurnal Tekno Kompak, vol. 14, no. 2, 2020, doi: 10.33365/jtk.v14i2.792.
G. Buana Putri, A. Khoir Diaz, and T. Sutabri, “Algoritma Machine Learning Untuk Klasifikasi Review Film ada IMDB 25 ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI REVIEW FILM PADA IMDB,” Dinamika Informatika, vol. 15, no. 1, 2023.
S. Samsir, A. Ambiyar, U. Verawardina, F. Edi, and R. Watrianthos, “Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 1, 2021, doi: 10.30865/mib.v5i1.2580.
A. Deolika and E. Taufiq Luthfi, “ANALISIS PEMBOBOTAN KATA PADA KLASIFIKASI TEXT MINING,” Jurnal Teknologi Informasi, vol. 3, no. 2, 2019.
Putri Ramayanti and Tata Sutabri, “PERBANDINGAN ALGORITMA NAÏVE BAYES DAN SVM UNTUK ANALISIS PENYALAHGUNAAN KEJAHATAN CARDING,” Jurnal Informatika Teknologi dan Sains, vol. 5, no. 1, 2023, doi: 10.51401/jinteks.v5i1.2231.
D. Novianti, “Implementasi Algoritma Naïve Bayes Pada Data Set Hepatitis Menggunakan Rapid Miner,” Paradigma - Jurnal Komputer dan Informatika, vol. 21, no. 1, 2019, doi: 10.31294/p.v21i1.4979.
M. F. Rahman, M. Ilham Darmawidjadja, and D. Alamsah, “KLASIFIKASI UNTUK DIAGNOSA DIABETES MENGGUNAKAN METODE BAYESIAN REGULARIZATION NEURAL NETWORK (RBNN),” 2017.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Qois Al Qorni Roy
![Creative Commons License](http://i.creativecommons.org/l/by-sa/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
An author who publishes in the J-ENSITEC (Journal of Engineering and Sustainable Technology) agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-ShareAlike 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal
- The author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgment of its initial publication in this journal.
- The author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work