The Level of Student Satisfaction with the Online Learning Process During a Pandemic Using the K-means Algorithm
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Abstract
The number of cases of Covid-19 in this pandemic era is increasing and getting out of control every day. This triggers the Indonesian government to set policies on schools with online learning methods. Of course, online learning cannot ensure that it runs smoothly in all circles because several factors hinder the learning process. The difficulty of the internet network, limited quotas, unfamiliarity with the use of learning media, and an unsupportive environment for conducting online learning are the obstacles to ineffective online learning. The purpose of this study was to determine the level of satisfaction with online learning during the pandemic. This study uses quantitative research methods with a descriptive approach. Quantitative research methods will be processed into data mining using the K-Means Clustering Algorithm. The clustering process is carried out to get the results of clustering the level of student satisfaction. The dataset was obtained from the results of the questionnaire by submitting statements of satisfaction and dissatisfaction. The cluster type is based on high, medium, and low class. The test results obtained a value with the final iteration, namely the level of satisfied statements is categorized as high with a value of 11.79 compared to the dissatisfied statement, which is categorized as moderate with a value of 7.46. In contrast, for the low category level, there is no value of 0.00 cluster results state that the category is satisfied with online learning with a value of 9.33.
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