Pengelompokan Pola Interaksi Pengguna Media Sosial Menggunakan Algoritma K-Means untuk Pemetaan Aktivitas Online
DOI:
https://doi.org/10.47065/jimat.v6i1.954Keywords:
Data Mining; Social Media; K-Means Algoritm; Interaction Patterns; Online Activity MappingAbstract
This study aims to cluster social media user interaction patterns in order to obtain a more structured and informative mapping of online activities. User activity on social media platforms is increasing and diverse, ranging from the frequency of uploads, the intensity of comments, the pattern of information dissemination, to responses to certain content. This complexity creates problems in understanding the characteristics of user behavior as a whole, especially when the resulting data is very large and unstructured. To address this challenge, this study applies the K-Means Clustering algorithm, one of the data mining methods that is effective in clustering data based on similar characteristics. The dataset used comes from user activities that include the number of posts, the number of likes, the number of comments, and the level of daily interactions. K-Means is used to divide the data into several clusters that represent the types of user activities, such as active, semi-active, and passive users. The results show that the K-Means algorithm is able to produce a clear and measurable mapping of online activities, with evaluation values ??using SSE and silhouette scores indicating optimal cluster formation. From the grouping process, Cluster 1 was obtained as passive users consisting of U001, U002, U005, U007, and U009 with a final centroid value of C1 of (0.13; 0.13; 0.10; 0.18), Cluster 2 as active users consisting of U003, U004, U006, and U010 with a final centroid value of C2 of (0.53; 0.55; 0.59; 0.49; 0.62), and Cluster 3 as very active users consisting only of U008 with a final centroid value of C3 of (1.00; 1.00; 1.00; 1.00).
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References
S. Suryanto and W. Andriyani, “Sentiment Analysis of X Platform on Viral ‘Fufufafa’ Account Issue in Indonesia Using SVM,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 19, no. 1, p. 95, 2025, doi: 10.22146/ijccs.104158.
E. Edward, “Identifikasi Berita HOAX Berbasis Web Menggunakan Algoritma C4.5,” J. Ilmu Komput. dan Sist. Inf., vol. 9, no. 1, p. 53, 2021, doi: 10.24912/jiksi.v9i1.11558.
T. Firmansyah, P. Poningsih, and S. R. Andani, “Analisis Clustering Algoritma K-Means Sebagai Rekomendasi Penambahan Koleksi Buku Di Perpustakaan Madrasah Tsanawiyah Negeri 2 Simalungun,” Zahra Bull. Big data, Data Sci. Artif. Intell., vol. 1, no. 1, pp. 44–48, 2022.
L. Al-Alawi, J. Al Shaqsi, A. Tarhini, and A. S. Al-Busaidi, “Using machine learning to predict factors affecting academic performance: the case of college students on academic probation,” Educ. Inf. Technol., vol. 28, no. 10, pp. 12407–12432, 2023, doi: 10.1007/s10639-023-11700-0.
L. Erlistya, P. S. Sumarna, and ..., “Implementasi Algoritma K-Means Clustering pada Studi Kasus Seleksi Mahasiswa Berprestasi menggunakan Visual Basic,” Pros. Semin. …, vol. 4, no. 2721, 2021.
H. Ani, D. Nofriansyah, and I. Mariami, “Implementasi Data Mining Untuk Pengelempokan Buku Di Perpustakaan Yayasan Nurul Islam Indonesia Baru Dengan Metode K-Means Clustering,” J. Cyber Tech, vol. 1, no. 1, 2021.
S. Hariyanti, “Pemanfaatan Media Sosial Tiktok Sebagai Sarana Promosi Kripik Jamur Tiram,” Al-Muraqabah J. Manag. Sharia Bus., vol. 2, no. 2, pp. 126–145, 2022.
F. Saefulloh, “Analisis Sentimen Masyarakat Terhadap E-Commerce Pada Media Sosial Twitter Menggunakan Metode Support Vector Machine (Svm) (Studi Kasus Shopee Dan Tokopedia),” S1 thesis, Univ. Muhammadiyah Purwokerto, vol. 8, no. 2502, pp. 79–88, 2023.
R. S. Wulandari and F. K. Sari, “Media Sosial sebagai Platform Pembelajaran Alternatif di Era Digital,” Pros. Nas. Pendidik. LPPM IKIP PGRI Bojonegoro, vol. 3, no. 1, pp. 279–288, 2023.
A. S. Sembiring and T. S. Alasi, “Penerapan Data Mining Menggunakan Algoritma Apriori Pada Peminjaman Buku di Perpustakaan Pada Pesantren Babul Ulum,” J. Armada Inform., vol. 7, no. 2, pp. 323–327, 2023.
T. P. Lestari, “Analisis Text Mining pada Sosial Media Twitter Menggunakan Metode Support Vector Machine (SVM) dan Social Network Analysis (SNA),” J. Inform. Ekon. Bisnis, vol. 4, no. 3, pp. 65–71, 2022, doi: 10.37034/infeb.v4i3.146.
I. Kanedi and E. Suryana, “Penerapan Metode K-Means Clustering Dalam Pengelompokan Data Pasien Rawat Inap Peserta BPJS Di Rumah Sakit Umum Daerah Kabupaten Kaur,” vol. 20, no. 2, pp. 493–500, 2024.
K. D. A. N. Knn, “Analisis clustering pada pemilihan tema vsga dengan metode k-means dan knn clustering analysis on vsga theme selection using k-means and knn methods,” Komputika J. Sist. Komput., vol. 7, no. 3, 2024.
C. Penerima, B. Kip, and S. Usanto, “Penerapan Data Mining Dengan Mengimplementasikan Algoritma K- Means Dalam Proses Clustering Untuk Pengelompokan Mahasiswa,” Build. Informatics, Technol. Sci., vol. 5, no. 1, pp. 70–85, 2023, doi: 10.47065/bits.v5i1.3411.
D. Aulia, M. Safii, and D. Suhendro, “Penerapan Algoritma K-Means dalam Proses Clustering Penilaian Kinerja Aparatur Sipil Negera di Sekretariat DPRD Pematangsiantar,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 6, no. 1, p. 47, 2021, doi: 10.30645/jurasik.v6i1.270.
M. Y. Matdoan, “Perbandingan Algoritma K-Means Dan K-Medoids Dalam Pengelompokkan Tingkat Kebahagiaan Provinsi Di Indonesia,” BULLET J. Multidisiplin Ilmu, vol. 1, no. 05, pp. 830–839, 2022.
R. Riadi and Mesran, “Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Analisa Penjualan Parfume,” J. Informatics, Electr. Electron. Eng., vol. 2, no. 4, pp. 138–145, 2023, doi: 10.47065/jieee.v2i4.1181.
A. T. Basalamah and R. Setyadi, “Penerapan Algoritma K-Means Clustering Pada Tingkat Penyelesaian Pendidikan Di Provinsi Indonesia,” J. Inform. dan Teknol. Komput., vol. 4, no. 2, pp. 114–121, 2023.
A. Supriyadi, A. Triayudi, and I. D. Sholihati, “Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 6, no. 2, pp. 229–240, 2021, doi: 10.29100/jipi.v6i2.2008.
M. Afrizal, I. Saputra, R. Satria, and Rahmaddeni, “Analisis Performa Algoritma K-Means Clustering untuk Segmentasi Pasar di UMKM Muhammad,” VISA J. Visions Ideas, vol. 5, no. 2, pp. 647–657, 2025.
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