Sentimen Analisis Terhadap Fitur Tiktok Shop Menggunakan Nae Bayes dan K-Nearest Neighbor

 (*)Sandi Saputra Hasibuan Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Angraini Angraini (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Eki Saputra (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Megawati Megawati (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: December 24, 2023; Published: January 10, 2024

Abstract

One of the features introduced by the TikTok platform is TikTok Shop, where sellers and buyers can interact easily through the live broadcast feature and promote products within the TikTok application without having to switch to other applications, as well as offering products at very cheap prices. However, this causes dissatisfaction from small traders who find it difficult to compete. Opinions about the TikTok Shop feature have created various responses from the public. This research aims to analyze public sentiment towards the TikTok Shop feature using Twitter as a data source. Applying the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms and dividing data using 10-Fold Cross Validation. Labeling was carried out using the clustering method using the K-Means algorithm, divided into three categories positive, negative and neutral. The addition of qualitative data analysis techniques with thematic analysis method in this research aims to find patterns or themes in the data. The labeling results show that 75% of the total data expresses negative sentiment towards the TikTok Shop feature. And the results of the thematic analysis found that the main theme, namely "Inappropriate regulations", covers 32% of the data, with a total of 754. This research concludes that the KNN method is superior to NBC, with better accuracy, precision and recall. This is in line with previous research that KNN is superior to nae Bayes, but other research shows the opposite where nae Bayes is superior to KNN. Further research can be carried out to improve the performance of these two algorithms in sentiment analysis, for example by using more sophisticated preprocessing methods, more representative feature extraction, or more efficient optimization techniques.

Keywords


Sentiment Analysis; K-Nearest Neighbor; Naive Bayes Classifier; TikTok Shop

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References

D. E. Oktania and T. A. Indarwati, Pengaruh Perceived Usefulness, Perceived Ease Of Use, Dan Compatibility With Lifestyle Terhadap Niat Beli Di Social Commerce, Jurnal Ilmu Manajemen, vol. 10, no. 1, pp. 255267, Mar. 2022, doi: 10.26740/jim.v10n1.p255-267.

D. Amanah, D. Ansari Harahap, and M. Gunarto, Perceived Risk and Security in Creating Online Purchasing Decision at Marketplace in Indonesia, Journal of Applied Business and Economics (JABE), vol. 7, no. 2, pp. 162179, Dec. 2020, doi: 10.30998/jabe.v7i2.7553.

A. A. A. Sharabati, S. Al-Haddad, M. Al-Khasawneh, N. Nababteh, M. Mohammad, and Q. Abu Ghoush, The Impact of TikTok User Satisfaction on Continuous Intention to Use the Application, Journal of Open Innovation: Technology, Market, and Complexity, vol. 8, no. 3, Sep. 2022, doi: 10.3390/joitmc8030125.

I. Purwandani, N. O. Syamsiah, and S. Nurwahyuni, Perceived Usability Evaluation of TikTok Shop Platform Using the System Usability Scale, Sinkron, vol. 8, no. 3, pp. 13891399, Jul. 2023, doi: 10.33395/sinkron.v8i3.12473.

S. Septyaningsih, M. T. Multazam, and B. Sobirov, Legal Protection of Consumer Rights in Transactions at TikTok Shop: Unraveling New Legal Insights, Kosmik Hukum, vol. 23, no. 3, p. 248, Aug. 2023, doi: 10.30595/kosmikhukum.v23i3.17396.

M. Wongkar and A. Angdresey, Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler : Twitter, 2019 Fourth International Conference on Informatics and Computing (ICIC), Oct. 2019, doi: 10.1109/ICIC47613.2019.8985884.

F. Aftab et al., A Comprehensive Survey on Sentiment Analysis Techniques, International Journal of Technology, vol. 14, no. 6, pp. 12881298, 2023, doi: 10.14716/ijtech.v14i6.6632.

C. Sindhu, B. Sasmal, R. Gupta, and J. Prathipa, Subjectivity detection for sentiment analysis on Twitter data, Lecture Notes in Networks and Systems, vol. 130, pp. 467476, 2021, doi: 10.1007/978-981-15-5329-5_43.

A. J. Nair, G. Veena, and A. Vinayak, Comparative study of Twitter Sentiment on COVID - 19 Tweets, in Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, Institute of Electrical and Electronics Engineers Inc., Apr. 2021, pp. 17731778. doi: 10.1109/ICCMC51019.2021.9418320.

A. R. Atmadja, W. Uriawan, F. Pritisen, D. S. Maylawati, and A. Arbain, Comparison of Naive Bayes and K-nearest neighbours for online transportation using sentiment analysis in social media, in Journal of Physics: Conference Series, Institute of Physics Publishing, Dec. 2019. doi: 10.1088/1742-6596/1402/7/077029.

H. Wisnu, M. Afif, and Y. Ruldevyani, Sentiment analysis on customer satisfaction of digital payment in Indonesia: A comparative study using KNN and Nave Bayes, in Journal of Physics: Conference Series, Institute of Physics Publishing, Feb. 2020. doi: 10.1088/1742-6596/1444/1/012034.

H. Sudira, A. L. Diar, and Y. Ruldeviyani, Instagram Sentiment Analysis with Naive Bayes and KNN Exploring Customer Satisfaction of Digital Payment Services in Indonesia, 2019 International Workshop on Big Data and Information Security (IWBIS), Oct. 2019, doi: 10.1109/IWBIS.2019.8935700.

E. Utami, S. Raharjo, A. Dwi Hartanto, S. Adi, and A. Noor Ichsan, K-Nearest Neighbor and Naive Bayes Classifier Comparison for Individual Character Classification on Twitter, in 2020 2nd International Conference on Cybernetics and Intelligent System, ICORIS 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/ICORIS50180.2020.9320759.

F. A. Wenando, R. Hayami, Bakaruddin, and A. Y. Novermahakim, Tweet Sentiment Analysis for 2019 Indonesia Presidential Election Results using Various Classification Algorithms, in Proceeding - 1st International Conference on Information Technology, Advanced Mechanical and Electrical Engineering, ICITAMEE 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 279282. doi: 10.1109/ICITAMEE50454.2020.9398513.

S. D. Pramukti, A. Nugroho, and A. S. Sunge, Analisis Sentimen Masyarakat Dengan Metode Nave Bayes dan Particle Swarm, Techno.COM, vol. 21, no. 1, pp. 6275, Feb. 2022, doi: 10.33633/tc.v21i1.5332.

S. Pradha, M. N. Halgamuge, and T. Q. V. Nguyen, Effective Text Data Preprocessing Technique for Sentiment Analysis in Social Media Data, 2019 11th International Conference on Knowledge and Systems Engineering (KSE), 2019, pp. 18. doi: 10.1109/KSE.2019.8919368.

Sintia, S. Defit, and G. W. Nurcahyo, Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (Tf-Idf), Journal of Applied Engineering and Technological Science, vol. 2, no. 2, pp. 1421, May 2021, doi: 10.37385/jaets.v2i2.210.

Mustakim, M. Z. Fauzi, Mustafa, A. Abdullah, and Rohayati, Clustering of Public Opinion on Natural Disasters in Indonesia Using DBSCAN and K-Medoids Algorithms, in Journal of Physics: Conference Series, IOP Publishing Ltd, Feb. 2021. doi: 10.1088/1742-6596/1783/1/012016.

Suprianto, M. Fadlan, Muhammad, Y. Amaliah, and Mussallimah, Retrieval information using generalized vector space models and sentiment analysis using nave bayes classifier for evaluation of lecturers by students, in 2020 5th International Conference on Informatics and Computing, ICIC 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020. doi: 10.1109/ICIC50835.2020.9288584.

F. Firmansyah et al., Comparing Sentiment Analysis of Indonesian Presidential Election 2019 with Support Vector Machine and K-Nearest Neighbor Algorithm, in 6th International Conference on Computing, Engineering, and Design, ICCED 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020. doi: 10.1109/ICCED51276.2020.9415767.

I. A. Abu Amra and A. Y. A. Maghari, Students Performance Prediction Using KNN and Nave Bayesian, 8th International Conference on Information Technology (ICIT), 2017, pp. 909913. doi: 10.1109/ICITECH.2017.8079967.

A. R. Isnain, H. Sulistiani, B. M. Hurohman, A. Nurkholis, and Styawati, Analisis Perbandingan Algoritma LSTM dan Naive Bayes untuk Analisis Sentimen, JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 8, no. 2, Aug. 2022, doi: 10.26418/jp.v8i2.54704.

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