Cluster Analysis: K-Means Cluster In Entertainment Media Use Based On User Profile
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Abstrak
Entertainment media is no longer just for watching movies and hearing news, but more than that it is a bridge to access broader information and even a business. The purpose of this study is to classify the use of entertainment media using the Cluster Analysis approach in terms of user profiles. Cluster analysis is a statistical technique that functions in grouping data into certain groups with the understanding that certain data have similarities or certain characteristics that are close together. This study took directly with the number of respondents 70 people. In collecting data, the length of use of mobile phones in accessing entertainment media was taken, the most frequently accessed entertainment media, and the reasons for using access to entertainment media. The results obtained are three clusters, namely Cluster 1: contains respondents who have an age less than the population average, has an average job as a student and private sector, Cluster 2: contains respondents who have an age less than the population average, has an average job of the average student, Cluster 3: contains respondents who are older than the population average, have an average job as a civil servant and are not yet working. the use of entertainment media by respondents in the three existing clusters is very different from one cluster to another. Overall all variables have differences in the three clusters because the significance level is not more than 0.05.
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Cluster Analysis: K-Means Cluster In Entertainment Media Use Based On User Profile. (2023). TABAYYUN, 4(1), 192-207. https://doi.org/10.19109/tabayyun.v4i1.18375
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Articles
Cara Mengutip
Cluster Analysis: K-Means Cluster In Entertainment Media Use Based On User Profile. (2023). TABAYYUN, 4(1), 192-207. https://doi.org/10.19109/tabayyun.v4i1.18375
Referensi
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Doni, F. R., & Faqih, H. (2017). Perilaku Penggunaan Media Sosial Pada Kalangan Remaja. IJSE-Indonesian Journal on Software Engineering, 3(2).
Ediyanto, Mara, N., & Intisari, N. S. (2013). Pengklasifikasian Karakteristik Dengan Metode K-Means Cluster Analysis. Buletin Ilmiah Mat. Stat. Dan Terapannya (Bimaster).
Prasetio, T., & Hariyani, R. (2020). Pengaruh Motivasi Dalam Penggunaan Media Sosial Dan Pengetahuan Kewirausahaan Terhadap Minat Berwirausaha Pada Mahasiswa. Jurnal IKRA ITH Ekonomika, 3(3), 94=101.
Prima, R., E, Y. A., & Sutikno. (2020). Analisis Cluster Virus Corona (COVID-19) di Indonesia pada 2 Maret 2020 – 12 April 2020 dengan Metode K-Means Clustering. Public Health.
Santoso, S. (2014). Statistik Multivariat Edisi Revisi. In Jakarta: PT Elex Media Komputindo.
Talakua, M. W., Leleury, Z. A., & Taluta, A. W. (2017). ANALISIS CLUSTER DENGAN MENGGUNAKAN METODE K-MEANS UNTUK PENGELOMPOKKAN KABUPATEN/KOTA DI PROVINSI MALUKU BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA TAHUN 2014. BAREKENG: Jurnal Ilmu Matematika Dan Terapan. https://doi.org/10.30598/barekengvol11iss2pp119-128
Yulianto, S., & Hidayatullah, K. H. (2016). Analisis Klaster Untuk Pengelompokan Kabupaten/Kota Di Provinsi Jawa Tengah Berdasarkan Indikator Kesejahteraan Rakyat. Statistika.