Implementasi Data Mining Dalam Pengelompokan Perilaku Penggunaan Media Sosial Siswa SMA Menerapkan Algoritma K-Means
DOI:
https://doi.org/10.47065/jimat.v6i1.959Keywords:
Social Media; K-Means Clustering; Data Mining; Siswa SMAAbstract
The development of information and communication technology has encouraged the increasing use of social media among teenagers, especially high school students (SMA). Social media is not only used as a means of communication and entertainment, but also has the potential to influence students' learning behavior. Excessive use can reduce concentration and learning effectiveness, so analysis is needed to systematically identify patterns of social media usage behavior. This study aims to group the social media usage behavior of high school students using the K-Means Clustering algorithm. Data were obtained through a closed questionnaire with a numerical scale that includes three variables, namely the frequency of social media use, duration of use, and the number of social media platforms used. A total of 20 sample data were used to illustrate the calculation process. The analysis stages include data normalization using the Min-Max method, determining the number of clusters as many as three clusters (K = 3), calculating distances using Euclidean Distance, and iteratively updating the entroids. Based on the results of the K-Means algorithm calculations until reaching a convergent condition, the social media usage behavior of high school students was successfully grouped into three clusters, namely low usage with centroids (3.50; 1.25; 2.75), moderate usage with centroids (7.25; 3.25; 5.88), and high usage with centroids (12.25; 5.75; 8.88). These results indicate that the K-Means algorithm is effective in grouping students' social media usage behavior based on differences in access frequency, duration of use, and level of social media interaction.
Downloads
References
M. Hammam and T. Utomo, “CLUSTERING DATA SISWA PUTUS SEKOLAH DENGAN ALGORITMA K-MEANS DAN DBSCAN,” Inf. J. Ilm. Bid. Teknol. Inf. dan Komun., vol. 18, no. 2, pp. 150–159, 2023.
M. Sosial and D. Mining, “Pengelompokan ( Clustering ) Perilaku Penggunaan Media Sosial Siswa SMA Menggunakan Algoritma K-Means,” Inform. J. Ilmu Komput., vol. 3, no. 1, pp. 300–310, 2021.
E. W. Ambarsari, D. Fathudin, and G. Alfiani, “Utilizing K-Means Clustering to Understanding Audience Interest in SEO- Optimized Media Content,” J. Comput. Informatics Res., vol. 3, no. 2, pp. 208–214, 2024, doi: 10.47065/comforch.v3i2.1207.
S. Dewi, A. Kresnawati, S. Sopyanti, and A. Sulmainah, “Mapping Library User Behavior Base On K-Means Clustering Of Ma ’ soem University Student Pemetaan Perilaku Pengguna Perpustakaan Berbasis K-Means Pada Mahasiswa Universitas Ma ’ soem,” Bimaster Bul. Ilm. Mat. Stat. dan Ter., vol. 5, no. 2, pp. 130–136, 2025.
A. N. Fauzan, F. A. Wandi, A. Zuhair, N. Aiman, and M. Wati, “Pengelompokan Minat Akademik Siswa SMA Negeri 1 Loa Janan Menggunakan Metode Clustering K-means,” J. Bina Komput., vol. 9, no. 2, pp. 165–174, 2025.
A. E. Widjaja, A. Fransisko, and C. A. Haryani, “Text Mining Application with K-Means Clustering to Identify Sentiments and Popular Topics?: a Case Study of the three Largest Online Marketplaces in Indonesia,” Inform. J. Ilmu Komput., vol. 4, no. 4, pp. 441–453, 2023.
Y. Malode, A. Aylani, A. Bhardwaj, and D. Hajoary, “Comparative Analysis of Community Detection Algorithms on the SNAP Social Circles Dataset,” Int. J. Environ. Res. Public Health, vol. 1, no. 1, pp. 1–20, 2022.
D. M. Putra and F. F. Abdulloh, “Comparison of Clustering Algorithms?: Fuzzy C-Means , K-Means , and DBSCAN for House Classification Based on Specifications and Price,” J. Appl. Informatics Comput., vol. 8, no. 2, pp. 509–515, 2024.
M. N. Afrilia, N. Rahaningsih, R. D. Dana, and N. D. Nuris, “Optimasi Analisis Clustering Untuk Aktivitas Dan Respon Pengguna Media Sosial Dengan K-Means,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 148–155, 2024.
P. K. Berindikasi, M. A. Syawal, and B. A. Pradhana, “Implementation Of The K-Means Method For Grouping Words Indicative Of Cyberbullying In,” Inform. J. Tek. Inform. dan Multimed., vol. 9, no. 2, pp. 330–347, 2025, doi: 10.52362/jisicom.v9i2.2163.
R. Dila, S. Defit, and S. Arlis, “Analisis Algoritma K-Means Clustering Dalam Pengelompokan Prestasi Belajar Siswa Menengah Atas ( SMA ),” vol. 5, no. 5, pp. 1113–1119, 2025, doi: 10.47065/bulletincsr.v5i5.751.
F. Raihan, “Analisis Pengaruh Media Sosial X Terhadap Sikap dan Perilaku Remaja Menggunakan K-Means Clustering,” J. Indones. Manaj. Inform. Dan Komunikasi, vol. 5, no. 3, pp. 3138–3146, 2024.
N. U. NLPP Dewi, IN Purnama, “Klasterisasi Mahasiswa Berdasarkan Performa Akademik Menggunakan Algoritma K-Means pada RapidMiner,” J. Eng. Informatics, vol. 1, no. 1, pp. 46–50, 2025.
J. Evans, Y. Nataliani, S. Informasi, U. Kristen, S. Wacana, and J. O. N. Salatiga, “Analisis Perilaku Siswa pada Pembelajaran Daring Menggunakan k-Means Clustering dari Log Data Moodle pada SMPN 6 Salatiga,” JuSiTik J. Sist. dan Teknol. Inf. Komun., vol. 7, no. 2, pp. 43–49, 2024.
F. Aziz, B. K. Romadhan, and I. Z. Pratama, “Pengelompokan Berita Berdasarkan Kategori Like Action Pada Media Sosial Instagram Penamuda Menggunakan Algoritma K- means,” JuSiTik J. Sist. dan Teknol. Inf. Komun., vol. 1, no. 2, pp. 57–60, 2024.
C. Agusniar, S. M. Ulfa, and H. Rhomadhona, “Utilizing K-Means Clustering for Grouping Student Achievement Data to Evaluate Learning Activeness,” J. Adv. Comput. Knowl. Algorithms, vol. 2, no. 3, pp. 56–59, 2025.
M. Risnasari, N. Aulia, and L. Cahyani, “Clustering Of Student Learning Styles in the Industry 4 . 0 Using KMeans Algorithm,” J. Bina Komput., vol. 24, no. August, pp. 246–257, 2022.
Z. Gong and Y. Guo, “Social media use and academic performance among college students?: the chain mediating roles of social anxiety and fear of missing out and the moderating effect of teacher-student relationship,” J. Armada Inform., no. October, pp. 1–11, 2025, doi: 10.3389/fpsyg.2025.1649890.
O. Rizan, “The Influence of Social Media on Student Learning Behavior and Its Effects on Academic Achievement,” Inform. J. Tek. Inform. dan Multimed., vol. 14, pp. 244–249, 2025.
B. Long, “Social Media and Students ’ Academic Performance?: A Case Study of Students Pursuing Business Administration at Higher Education Institution in Phnom Penh , Cambodia,” J. Armada Inform., vol. 2, 2023.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Data Mining Dalam Pengelompokan Perilaku Penggunaan Media Sosial Siswa SMA Menerapkan Algoritma K-Means
ARTICLE HISTORY
Issue
Section
Copyright (c) 2026 Utari Utari, Muhammad Iqbal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are 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 an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their 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 published work (Refer to The Effect of Open Access).
