Utilizing K-Means Clustering to Understanding Audience Interest in SEO-Optimized Media Content

Erlin Windia Ambarsari; Dedin Fathudin; Gravita Alfiani

doi:10.47065/comforch.v3i2.1207

Erlin Windia Ambarsari * Universitas Indraprasta PGRI, Indonesia
Dedin Fathudin Universitas Pamulang, Indonesia
Gravita Alfiani Media Have Fun, Indonesia

DOI: https://doi.org/10.47065/comforch.v3i2.1207

Keywords: K-Means; Elbow; SEO; Audience

Abstract

This study observes k-means clustering for segmenting SEO data to understand audience interests, identifying the elbow method as crucial for determining the optimal number of clusters. It highlights notable differences in content engagement across clusters, emphasizing the need for refined SEO strategies and a deeper understanding of audience segmentation. Despite challenges like SEO's dynamic nature and data reliance, this methodology provides a strong foundation for enhancing content strategies. Future research suggestions include cross-platform data integration, longitudinal studies, sentiment analysis, content experimentation, user experience (UX) focus, and monitoring algorithm updates to develop more adaptive content and SEO strategies aligned with changing audience behaviors.

References

R. Andrianto, “Ini yang Bikin Sri Lanka Chaos, Semoga Tak Terjadi di RI,” CNBC Indonesia, Jakarta, Jul. 11, 2022. Accessed: Mar. 04, 2024. [Online]. Available: https://www.cnbcindonesia.com/news/20220711141658-4-354659/ini-yang-bikin-sri-lanka-chaos-semoga-tak-terjadi-di-ri

OECD, "Mitigating the impact of COVID-19 on tourism and supporting recovery," 2020.

A. A. Aburumman, "COVID-19 impact and survival strategy in business tourism market: the example of the UAE MICE industry," Humanit Soc Sci Commun, vol. 7, no. 1, p. 141, 2020, doi: 10.1057/s41599-020-00630-8.

C. Lopezosa, L. Codina, J. Díaz-Noci, and J.-A. Ontalba, "SEO and the digital news media: From the workplace to the classroom," Comunicar, vol. 28, no. 63, pp. 65–75, Apr. 2020, doi: 10.3916/C63-2020-06.

D. Giomelakis, C. Karypidou, and A. Veglis, "SEO inside Newsrooms: Reports from the Field," Future Internet, vol. 11, no. 12, p. 261, Dec. 2019, doi: 10.3390/fi11120261.

R. S. Bhandari and S. Bansal, "An Analysis Between Search Engine Optimization Versus Social Media Marketing Affecting Individual Marketer's Decision--Making Behavior," Jindal Journal of Business Research, vol. 8, no. 1, pp. 78–91, Jun. 2019, doi: 10.1177/2278682119829607.

M. Poturak, D. Keco, and E. Tutnic, "Influence of search engine optimization (SEO) on business performance," International Journal of Research in Business and Social Science (2147- 4478), vol. 11, no. 4, pp. 59–68, Jun. 2022, doi: 10.20525/ijrbs.v11i4.1865.

M. A. Syakur, B. K. Khotimah, E. M. S. Rochman, and B. D. Satoto, "Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster," IOP Conf Ser Mater Sci Eng, vol. 336, p. 012017, Apr. 2018, doi: 10.1088/1757-899X/336/1/012017.

A. Zaki, I. Irwan, and I. A. Sembe, "Penerapan K-Means Clustering dalam Pengelompokan Data (Studi Kasus Profil Mahasiswa Matematika FMIPA UNM)," Journal of Mathematics Computations and Statistics, vol. 5, no. 2, p. 163, Oct. 2022, doi: 10.35580/jmathcos.v5i2.38820.

I. A. Yorinda and A. B. Raharjo, "Analysis of Customer Profile Charasteristic with Credit Quality Using the Clustering Method for Risk Mitigation and Small Medium Enterprise Credit Portofolio Expansion Planning," Business and Finance Journal, vol. 8, no. 2, pp. 181–191, Nov. 2023, doi: 10.33086/bfj.v8i2.5225.

F. Dikarya and S. Muharni, “Penerapan Algoritma K-Means Clustering untuk Pengelompokan Universitas Terbaik di Dunia,” Jurnal Informatika, vol. 22, no. 2, pp. 124–131, Dec. 2022, doi: 10.30873/ji.v22i2.3324.

D. Triyansyah and D. Fitrianah, “Analisis Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing,” Jurnal Telekomunikasi dan Komputer, vol. 8, no. 3, p. 163, Oct. 2018, doi: 10.22441/incomtech.v8i3.4174.

A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, "K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data," Inf Sci (N Y), vol. 622, pp. 178–210, Apr. 2023, doi: 10.1016/j.ins.2022.11.139.

N. Jayanti, C. G. Selan, and M. W. Prihatmono, “Analisis Data Pengunjung Mall Nipah Mendukung Strategi Digital Marketing Menggunakan K-Means Clustering,” Jurnal Penelitian Inovatif, vol. 2, no. 1, pp. 209–220, May 2022, doi: 10.54082/jupin.65.

I. Ramadhaniati, "Product Clustering using K-MEANS Method in CV. JAYA ABADI," Jurnal TAM (Technology Acceptance Model) , vol. 14, no. 1, pp. 91–97, 2023.

A. Ali and S. A. M. Uktutias, “Optimasi Hasil Clustering Data Rekam Medis Balita di Desa Jumput Rejo dengan Metode Elbow dalam Menunjang Program Pemerintah Mengatasi Stunting,” Joutica : Journal of Informatic Unisla, vol. 8, no. 1, 2023.

K. C. Gunawan, “Analisis Peserta Online Test dengan Pembentukan Cluster dengan Algoritma K-Means dan Analisis Tabulasi,” Jurnal Titra, vol. 8, no. 2, pp. 353–360, 2020.

H. Humaira and R. Rasyidah, "Determining The Appropiate Cluster Number Using Elbow Method for K-Means Algorithm," in Proceedings of the 2nd Workshop on Multidisciplinary and Applications (WMA) 2018, 24-25 January 2018, Padang, Indonesia, EAI, 2020. doi: 10.4108/eai.24-1-2018.2292388.

C. Shi, B. Wei, S. Wei, W. Wang, H. Liu, and J. Liu, "A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm," EURASIP J Wirel Commun Netw, vol. 2021, no. 1, p. 31, 2021, doi: 10.1186/s13638-021-01910-w.

D. A. I. C. Dewi and D. A. K. Pramita, “Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali,” Matrix : Jurnal Manajemen Teknologi dan Informatika, vol. 9, no. 3, pp. 102–109, Nov. 2019, doi: 10.31940/matrix.v9i3.1662.