Analysis of The Theme Clustering Algorithm Using K-Means Method

Erwin Dwika Putra (Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Bengkulu)
Muhammad Husni Rifqo (Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Bengkulu)
Dwita Deslianti (Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Bengkulu)
Krismiyani Krismiyani (Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhammadiyah Bengkulu)

Jurnal Komputer, Informasi dan Teknologi
Vol. 2 No. 2 (2022): Desember

The title of this research is the analysis of the thesis theme clustering algorithm using the k-means method. The main problem is how we can find out which theme is most in demand by thesis students at the Faculty of Engineering, University of Muhammadiyah Bengkulu. This clustering uses the K-means method. The K-Means method was chosen because this method is one of the non-hierarchical data clustering methods that seeks to partition data into two or more clusters with the same characteristics included in the same cluster. The purpose of this research is to help prospective students who will write their thesis in knowing which themes are more interested in them.