Implementasi Algoritma Hierarchical Clustering dan Non-Hierarchical Clustering untuk Pengelompokkan Pengguna Media Sosial

Zulkifli Alamtaha (a:1:{s:5:"en_US"s:63:"Jurusan Matematika, Fakultas MIPA, Universitas Negeri Gorontalo"})
Ismail Djakaria (Jurusan Matematika, Fakultas MIPA, Universitas Negeri Gorontalo)
Nisky Imansyah Yahya (Jurusan Matematika, Fakultas MIPA, Universitas Negeri Gorontalo)

ESTIMASI: Journal of Statistics and Its Application
Vol. 4, No. 1, Januari, 2023 : Estimasi

Social media is a means to interact with other people through sentences, pictures and videos online. Excessive use of social media has a negative impact on mental health. The grouping process in this study was carried out to see the level of social media use in Bone Bolango Regency. Before grouping, data pre-processing is carried out and the optimal number of clusters is determined using the Silhoutte index. The optimal cluster results obtained are two clusters for all methods. After that, grouping is done using Hierarchical Clustering and Non-Hierarchical Clustering Algorithms. The Hierarchical Clustering algorithm consists of two methods, namely the single linkage method and the complete linkage method. The Non-Hierarchical Clustering Algorithm consists of two methods, namely the K-Means and K-Medoids methods. The next step is to determine the best method using the Davies-Bouldin Index (DBI). The smaller the DBI value, the better the method used. The smallest DBI value is obtained in the complete linkage method. The grouping results for cluster 1 consisted of 70 respondents and cluster 2 consisted of 80 respondents.