Abstract:
It is important to protect all employees from contacting COVID-19 since they
want to continue their careers. Therefore, the remote working process has been
sped up for continuing the work without any interruptions during this Covid-
19 pandemic. Many organizations have been persuaded by the pandemic that
remote work has benefits for a successful organization. The objective of the
study is to identify the employees’ ideas and assess their sense for continuing
the remote working concept in the post-COVID-19 pandemic. The study used
a random sample of 325 workers in private and public sectors who did remote
work during the pandemic and collected data via questionnaires through
Google form. Information gain ranking was applied to the ranking of attributes
and removed unnecessary data. After the pre-processing, the prediction
models were generated using the WEKA tool, and 66% of the percentage split
used with six different classification algorithms such as Support Vector
Machine, Naive Bayes, Logistic Regression, Decision Tree, Multi-Layer
Perceptron, and Random Forest and ensemble learning algorithm that
combined above six algorithms using an average of probabilities through vote
algorithm. Based on the evaluation results of accuracy, recall, precision, fmeasure,
and error values, the ensemble learning algorithm outperforms the
other six algorithms by 90%. According to the data set, the majority of
employees prefer the remote working concept. Using this prediction model,
we can also assess the employee's sense and compatibility for the remote
working concept. In the future, we plan to expand the data set and conduct
more evaluations using deep learning.
