Sentimen Komentar Universitas Pelita Harapan Pada TikTok Menggunakan Metode K-Nearest Neighbor

Authors

  • Robert Wijaya Universitas Pelita Harapan
  • Albert Suwandhi Universitas IBBI

DOI:

https://doi.org/10.54259/jdmis.v2i1.2418

Keywords:

Sentiment Analysis, KNN, TikTok, Comment Sentiment, Analisis Sentimen Komentar

Abstract

In the evolving digital era, social media, particularly TikTok, has become a pivotal platform for information sharing and communication, including by educational institutions such as Universitas Pelita Harapan (UPH). The use of TikTok at UPH has generated diverse comments that require effective management, prompting this research to develop sentiment by using the K-Nearest Neighbor (KNN) algorithm. This study aims to address two main issues: analyzing the accuracy of the K-Nearest Neighbor algorithm in sentiment of comment sentences and measuring the performance of the K-Nearest Neighbor algorithm in calculating analysis results on comment sentences. This research employs the KNN method with a dataset of 1213 entries from 2021 to 2023 containing keywords related to UPH from TikTok platform content. The study is managed and conducted on Google Colab using the Python programming language. Based on the results of training and testing data, an accuracy of 91% is obtained, with precision at 93%, recall at 91%, and an f-1 score of 92%. From the performance of the KNN algorithm, it can be concluded that the KNN method can classify sentiment in comments

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References

S. Widi, “Pengguna Media Sosial di Indonesia Sebanyak 167 Juta pada 2023,” 2023. https://dataindonesia.id/internet/detail/pengguna-media-sosial-di-indonesia-sebanyak-167-juta-pada-2023

V. S. Virginia, “Perlindungan Hukum Korban Yang Dirugikan Akibat Pencemaran Nama Baik di Media Sosial Tiktok,” Supremasi J. Huk. , vol. 5, no. 02, pp. 134–143, 2021, [Online]. Available: https://jateng.tribunnews.com

Andi Saadillah, Andi Haryudi, Muhammad Reskiawan, and Alam Ikhsanul Amanah, “Penggunaan Bahasa Sarkasme Netizen di Media Sosial,” J. Onoma Pendidikan, Bahasa, dan Sastra, vol. 9, no. 2, pp. 1437–1447, 2023, doi: 10.30605/onoma.v9i2.2367.

R. Selgianita and M. N. Antono, “Disfemisme Warganet dalam Kolom Komentar Media Sosial Instagram @Kpipusat (Kajian Semantik),” J. Educ. Lang. Lit., vol. 1, no. 1, pp. 9–19, 2023, doi: 10.21107/jell.v1i1.19386.

D. Menur, “Model Online Learning dalam Mendukung Keterampilan Menulis Descriptive Text Peserta Didik pada Sosial Media,” Kurikula, 2020. https://www.neliti.com/publications/406832/model-online-learning-dalam-mendukung-keterampilan-menulis-descriptive-text-pese (accessed Sep. 15, 2020).

J. Florensius Sianipar, Y. R. Ramadhan, and I. Jaelani, “Analisis Sentimen Pembangunan Kereta Cepat Jakarta-Bandung di Media Sosial Twitter Menggunakan Metode Naive Bayes,” Media Online), vol. 4, no. 1, pp. 360–367, 2023, doi: 10.30865/klik.v4i1.1033.

A. Baita and N. Cahyono, “Analisis Sentimen Mengenai Vaksin Sinovac Menggunakan Algoritma Support Vector Machine (Svm) Dan K-Nearest Neighbor (Knn),” Infos, vol. 4, no. 2, pp. 42–42, 2021.

R. Puspitasari, Y. Findawati, M. A. Rosid, P. S. Informatika, and U. M. Sidoarjo, “Sentiment Analysis of Post-Covid-19 Inflation Based on Twitter Using the K-Nearest Neighbor and Support Vector Machine Analisis Sentimen Terhadap Inflasi Pasca Covid-19 Berdasarkan Twitter Dengan Metode Klasifikasi K-Nearest Neighbor Dan,” vol. 4, no. 4, pp. 1–11, 2023.

N. L. W. S. R. Ginantra, C. P. Yanti, G. D. Prasetya, I. B. G. Sarasvananda, and I. K. A. G. Wiguna, “Analisis Sentimen Ulasan Villa di Ubud Menggunakan Metode Naive Bayes, Decision Tree, dan K-NN,” J. Nas. Pendidik. Tek. Inform., vol. 11, no. 3, pp. 205–215, 2022, doi: 10.23887/janapati.v11i3.49450.

S. Qaiser and R. Ali, “Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents,” Int. J. Comput. Appl., vol. 181, no. 1, pp. 25–29, 2018, doi: 10.5120/ijca2018917395.

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Published

2024-02-05

How to Cite

Wijaya, R., & Suwandhi, A. (2024). Sentimen Komentar Universitas Pelita Harapan Pada TikTok Menggunakan Metode K-Nearest Neighbor. JDMIS: Journal of Data Mining and Information Systems, 2(1), 26–36. https://doi.org/10.54259/jdmis.v2i1.2418

Issue

Vol. 2 No. 1 (2024): February 2024

Section

Articles

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Copyright (c) 2024 Robert Wijaya, Albert Suwandhi

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