Prediction of Work From Home Post COVID-19 using Classification Model
7th International Conference on Informatics and Computing, ICIC 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2235427
ABSTRACT
This study aims to predict the continuance of the adoption of WFH after COVID-19 and other factors that also influence these preferences. This study was conducted using data mining techniques with the CRISP-DM framework based on the Decision Tree, Naïve Bayes, and Random Forest Algorithms. The dataset was taken based on a questionnaire survey distributed online via Google Forms with a target of 200 respondents and returned from 183 respondents from four divisions. The results of this study indicate the Decision Tree model has the best performance with an accuracy of 85.45%. Based on the prediction and questionnaire results, employees tend to agree to continue implementing WFH after COVID-19 with a Hybrid working model. These preferences influence work improvement, employee performance, and work environment. © 2022 IEEE.
classification model; CRISP-DM; GORP Kompas Gramedia; Predictive Analytics; Work From Home; Data mining; Decision trees; Personnel; Classification models; Data-mining techniques; Decision-tree model; Google+; GORP kompa gramedium; Naive bayes; Questionnaire surveys; Random forest algorithm; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
/
Prognostic study
/
Randomized controlled trials
/
Reviews
Topics:
Long Covid
Language:
English
Journal:
7th International Conference on Informatics and Computing, ICIC 2022
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS