Intelligent Telework Internet Cost Requirement Modeling Using Optimizable Machine Learning Algorithms
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)
(2022), pp. 1-6
Ryan Rhay P. Vicerra
a
,
Rex Paolo C. Gamara
b
,
Jayne Lois G. San Juan
c
,
Argel A. Bandala
d
,
Ronnie Concepcion
a
,
Elmer P. Dadios
a
,
Pocholo James M. Loresco
d
,
Jason E. Espanola
a
,
Alvin Culaba
e
,
Andres Philip Mayol
a
a Department of Manufacturing Engineering and Management, De La Salle University, Manila, Philippines
b Department of Electronics and Computer Engineering, De La Salle University, Manila, Philippines
c Department of Industrial and Systems Engineering, De La Salle University, Manila, Philippines
d Electrical and Electronics Engineering Department, FEU Institute of Technology, Manila, Philippines
e Department of Mechanical Engineering, De La Salle University, Manila, Philippines
Abstract: The COVID-19 pandemic has caused disruption to the economy due to the increasing infection that affects the workforce in different sectors. The Philippine government has imposed lockdowns to control the spread of infection. This urged the different sectors to implement flexible work schedules or work from home setup. A work-from-home (WFH) setup burdens both the employee and employer by installing different equipment set-ups such as WiFi-equipped laptops, computers, tablets, or smartphones. However, the internet stability in some of the areas in the Philippines is not yet reliable. In this study, an application is used collect survey information and provide an estimate of the telework internet cost requirement of a given government employee or a given government employee implementing a work-from-home set up in their respective household. This involves survey results from different respondents who are currently on a work-from-home setup and significant factors from the survey have been analyzed using machine learning (ML) algorithms. Among the machine learning algorithms used, the ensemble bagged trees model outperformed the other ML models. This work can be extended by incorporating a wider scope of datasets from different industry doing work from home set-up. In addition, in terms of education, it is also recommended to determine the WFH set up not just with the government employee and employer but to also extend this into the education side.