Classification of Teachers and Lecturers Engagement on Webinar during the Pandemic using the Utilization of Big Data
Abstract
The Pandemic had a big impact on education in Indonesia and also in the world. In early 2020, during this pandemic, face-to-face meetings have turned into virtual or online meetings for both the learning process and seminars or workshops. The rapid development of technology supports this change in the world of education, this can be seen from the number of online seminars conducted to improve the competence of lecturers or teachers. The development of this online seminar allows the circulation of information that is increasingly large, fast, and almost unlimited by time and space. This causes a large amount of information to be scattered in the virtual world in various fields. With this very fast information technology, trillions of bytes of data are created every day from various sources such as on social media, especially those related to applications that are often used in website-based seminar media. This is called unstructured big data. In this study, big data will be implemented to classify educators' engagement of online seminar participants during an early pandemic. The activity stages in big data management and data processing support are acquired, accessed, analytical, and applied. The method for this study is the Adaptive Neuro-Fuzzy Inference System (ANFIS) to classify the engagement of teachers and lecturers an online seminar. The results of the training error obtained from ANFIS are 0.273482 with the ANFIS structure 4-12-12-12-1 or 4 inputs, 12 hidden layers, and 1 output.
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References
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