Segmentasi Perilaku Belajar Mahasiswa Selama Masa Pandemi Covid-19 Berbasis K-Means - Fitri Marisa; Anastasia Lidya Maukar; Husri Sidi; Rivaldo Tito Lamberto Da Silva; Widiya Nur Permata; Achmad Aziz Wahdana (SMARTICS Journal, 2021)
Since the Covid-19 virus pandemic which has the long name Corona Virus Disease-19 hit in many countries, which is Indonesia, their government is currently making various efforts and steps to prevent the transmission of the Corona virus in our homeland by avoiding crowds, Therefore, the government carried out instructions to deal with the crowd by closing or resting or carrying out all activities from home, this method was to avoid the spread of Covid-19. This study use the K-Means Clustering method. The purpose of making this journal is to find a solution to evaluate the existing online learning system based on Student Learning Behavior Segmentation During the K-Means-Based Covid-19 Pandemic. Survey technique will be used in this research, a survey is used to obtain data or information from a number of respondents regarding the existence of an issue or topic. The data is obtained from filling out questions that have been shared with all respondents through a google form which will be processed later. The data that has been collected based on the data will use effective clustering regarding online learning in 4 clusters. The researchers report that the K-Means algorithm can be used to detect patterns from the data. Segmentation of student learning behavior during the Covid-19 pandemic based on K-Means based on the levels of "very good", "Good", "enough", "Poor", "very poor" on Student Learning Behavior During the Covid-19 Pandemic based K-Means.