Sistem Klasifikasi Strata Kelas Peserta Kursus berbasis web menggunakan algoritma K-Means - Maulida, Vivi; Mulyani, Neni; Sibuea, Mustika Fitri Larasati (Jurnal Pendidikan Informatika (EDUMATIC), 2024)
The increasing number of participants in online courses has driven the development of effective systems to manage and classify their data. The objective of this research is to develop a web-based class strata classification system for course participants using the K-Means algorithm. This research is developmental in nature, employing the waterfall model. We implemented this model through the stages of analysis, design, implementation, and testing. The data used were course participants from the Lembaga Swadaya Training Centre from 2013 to 2024. The system testing we developed utilized the black box method. The K-Means algorithm was chosen for its ability to cluster data without supervision, which is suitable for processing large and heterogeneous data from course participants. The data analysis results show that there are 2 clusters of class strata data: elementary, university, and general (C1) and junior high and high school (C2). Furthermore, our findings also include a web-based classification system integrated with the K-Means algorithm. System testing also showed that the system functions as intended according to the design and requirements analysis. This system can assist relevant parties in making decisions for promoting the market share of course participants.