Implementasi Text Clustering Terkait Pilpres 2024 Menggunakan Metode K-Means

Authors

  • Miquel Yosafat Universitas Kristen Immanuel Yogyakarta
  • Jatmika

DOI:

https://doi.org/10.61179/jurnalinfact.v8i01.496

Keywords:

Presidential Candidate, Clustering, K-Means, Presidential Election, Twitter

Abstract

This research focuses on implementing the K-Means Clustering method to analyze public opinion regarding the 2024 presidential election. The K-Means algorithm, as a data mining method without direction, is used to group opinion data that has similar characteristics. The results of the cluster analysis confirmed the absence of text that was sarcastic or sarcastic in the Twitter data taken. Clusters were divided and categorized based on the text approach, and the results showed that the word "pilpres" appeared the most with a total count of 1778, while the word "ahy" appeared the least with a total count of 15. This research provides in-depth insight into public perceptions of the 2024 presidential election through analysis opinion data clusters on social media.

References

Adhe, D., Rachman, C., Goejantoro, R., Deny, F., & Amijaya, T. (2020). Implementasi Text Mining Pengelompokkan Dokumen Skripsi Menggunakan Metode K-Means Clustering Implementation Of Text Mining For Grouping Thesis Documents Using K-Means Clustering. Jurnal EKSPONENSIAL, 11(2).

Aulia, D., Safii, M., Suhendro, D., Studi, M. P., Informasi, S., Tunas Bangsa, S., & Tunas Bangsa, A. (n.d.). Penerapan Algoritma K-Means dalam Proses Clustering Penilaian Kinerja ASN. 6, 47. https://tunasbangsa.ac.id/ejurnal/index.php/jurasik

Fitria Rini. (2015). BADAN PENGAWAS PEMILU DALAM SISTEM KETATANEGARAAN INDONESIA DALAM PERSPEKTIF KOMUNIKASI POLITIK.

Fridom Mailo, F., Lazuardi, L., Manajemen dan kebijakan Kesehatan Fakultas Kedokteran, D., Masyarakat dan Keperawatan Universitas Gadjah Mada, K., Sistem Informasi Manajemen Kesehatan Fakultas Kedokteran, D., Masyarakat dan Keperawatan, K., & Gadjah Mada, U. (2019). Analisis Sentimen Data Twitter Menggunakan Metode Text Mining Tentang Masalah Obesitas di Indonesia. In Jurnal Sistem Informasi Kesehatan Masyarakat Journal of Information Systems for Public Health (Vol. 4, Issue 1).

Han, J., Kamber, M., Melton, J., Buxton, S., Teorey, T. J., Lightstone, S. S., Nadeau, T. P., Celko, J., Witten, I., Frank, E., Simsion, G. C., Witt, G. C., Schiller, J., Voisard, A., Halpin, T., Evans, K., Hallock, P., Maclean, B., Ceri, S., … Voisard, A. (n.d.). Designing Data-Intensive Web Applications.

Handoko Koko. (2016). PENERAPAN DATA MINING DALAM MENINGKATKAN MUTU PEMBELAJARAN PADA INSTANSI PERGURUAN TINGGI MENGGUNAKAN METODE K-MEANS CLUSTERING (STUDI KASUS DI (STUDI KASUS DI PROGRAM STUDI TKJ AKADEMI KOMUNITAS SOLOK SELATAN). 2.

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Published

2024-01-30

How to Cite

[1]
Miquel Yosafat and Jatmika, “Implementasi Text Clustering Terkait Pilpres 2024 Menggunakan Metode K-Means”, IIJC, vol. 8, no. 01, pp. 6–12, Jan. 2024.

Issue

Vol. 8 No. 01 (2024): Jurnal Sains dan Komputer

Section

Articles

License

Copyright (c) 2024 Miquel Yosafat

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