Improved Artificial Neural Network with High Precision for Predicting Burnout among Managers and Employees of Start-Ups during COVID-19 Pandemic

• Published in: Electronics (Basel) Vol. 12; no. 5; p. 1109
• Main Authors: Sutrisno, Sutrisno, Khairina, Nurul, Syah, Rahmad B. Y, Eftekhari-Zadeh, Ehsan, Amiri, Saba
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Artificial neural networks; Burn out (Psychology); Burnout; Coronaviruses; COVID-19; Demographic variables; Employees; Epidemics; Network reliability; Neural networks; Neurons; Pandemics; Psychological aspects; Workers; Indonesia
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12051109

Notwithstanding the impact that the Coronavirus pandemic has had on the physical and psychological wellness of people, it has also caused a change in the psychological conditions of many employees, particularly among organizations and privately owned businesses, which confronted numerous limitations because of the unique states of the pandemic. Accordingly, the current review expected to implement an RBF neural network to dissect the connection between demographic variables, resilience, Coronavirus, and burnout in start-ups. The examination technique was quantitative. The statistical populace of the review is directors and representatives of start-ups. In view of the statistical sample size of the limitless community, 384 of them were investigated. For information gathering, standard polls incorporating MBI-GS and BRCS and specialist-made surveys of pressure brought about by Coronavirus were utilized. The validity of the polls was affirmed by a board of specialists and their reliability was affirmed by Cronbach’s alpha coefficient. The designed network structure had ten neurons in the input layer, forty neurons in the hidden layer, and one neuron in the output layer. The amount of training and test data were 70% and 30%, respectively. The output of the neural network and the collected results were compared with each other, and the designed network was able to classify all the data correctly. Using the method presented in this research can greatly help the sustainability of companies.