Staff View: Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]

Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]

Employee working productivity prediction is vital for effective resource allocation, increased productivity, and upholding a high-performance culture in organizations. However, predicting employee productivity and understanding the root factors influencing working performance pose significant challe...

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Main Authors:	Razali, Mohd Norhisham, Ibrahim, Norizuandi, Hanapi, Rozita, Mohd Zamri, Norfarahzila, Abdul Manaf, Syaifulnizam
Format:	Article
Language:	English
Published:	UiTM Cawangan Perlis 2023
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/86885/1/86885.pdf https://ir.uitm.edu.my/id/eprint/86885/
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id	my.uitm.ir.86885
record_format	eprints
spelling	my.uitm.ir.868852023-11-14T09:11:38Z https://ir.uitm.edu.my/id/eprint/86885/ Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.] jcrinn Razali, Mohd Norhisham Ibrahim, Norizuandi Hanapi, Rozita Mohd Zamri, Norfarahzila Abdul Manaf, Syaifulnizam Algorithms Employee working productivity prediction is vital for effective resource allocation, increased productivity, and upholding a high-performance culture in organizations. However, predicting employee productivity and understanding the root factors influencing working performance pose significant challenges. Traditional human resource management practices often lack data-driven insights, resulting in poor resource allocation and productivity enhancement strategies. To address these challenges, we developed a predictive model using machine learning techniques to determine employee productivity within organizations. Data from an academic institution were collected and pre-processed by encoding relevant features before applying various machine learning predictive models. Experimental results revealed that the linear regression model achieved the best performance in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), with values of 0.4878 and 0.4682, respectively. The research findings also highlighted attributes that are imperative in predicting employee performance. Attributes such as "Department," "Actual Productive hours," "Internet Speed," and "COVID-19 adoption month" emerged as highly influential factors across multiple ranking techniques. The data visualization provided valuable insights into various aspects of employee performance, such as productivity trends before and after the pandemic, departmental performance, internet connectivity's impact on productivity, age-related trends, overtime distribution, and promotion rates. Organizations can use this data to inform workforce planning, address specific challenges in departments, and cultivate an inclusive work environment. By regularly assessing productivity data and implementing recommended strategies, organizations can enhance productivity, create a conducive work environment, and support employee well-being and growth. Future research can explore more advanced machine learning algorithms, incorporate time-series analysis for temporal dependencies, and expand data collection from diverse organizational settings to improve the generalizability of predictive models. UiTM Cawangan Perlis 2023 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/86885/1/86885.pdf Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]. (2023) Journal of Computing Research and Innovation (JCRINN) <https://ir.uitm.edu.my/view/publication/Journal_of_Computing_Research_and_Innovation_=28JCRINN=29/>, 8 (2): 23. pp. 235-245. ISSN 2600-8793
institution	Universiti Teknologi Mara
building	Tun Abdul Razak Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Mara
content_source	UiTM Institutional Repository
url_provider	http://ir.uitm.edu.my/
language	English
topic	Algorithms
spellingShingle	Algorithms Razali, Mohd Norhisham Ibrahim, Norizuandi Hanapi, Rozita Mohd Zamri, Norfarahzila Abdul Manaf, Syaifulnizam Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]
description	Employee working productivity prediction is vital for effective resource allocation, increased productivity, and upholding a high-performance culture in organizations. However, predicting employee productivity and understanding the root factors influencing working performance pose significant challenges. Traditional human resource management practices often lack data-driven insights, resulting in poor resource allocation and productivity enhancement strategies. To address these challenges, we developed a predictive model using machine learning techniques to determine employee productivity within organizations. Data from an academic institution were collected and pre-processed by encoding relevant features before applying various machine learning predictive models. Experimental results revealed that the linear regression model achieved the best performance in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), with values of 0.4878 and 0.4682, respectively. The research findings also highlighted attributes that are imperative in predicting employee performance. Attributes such as "Department," "Actual Productive hours," "Internet Speed," and "COVID-19 adoption month" emerged as highly influential factors across multiple ranking techniques. The data visualization provided valuable insights into various aspects of employee performance, such as productivity trends before and after the pandemic, departmental performance, internet connectivity's impact on productivity, age-related trends, overtime distribution, and promotion rates. Organizations can use this data to inform workforce planning, address specific challenges in departments, and cultivate an inclusive work environment. By regularly assessing productivity data and implementing recommended strategies, organizations can enhance productivity, create a conducive work environment, and support employee well-being and growth. Future research can explore more advanced machine learning algorithms, incorporate time-series analysis for temporal dependencies, and expand data collection from diverse organizational settings to improve the generalizability of predictive models.
format	Article
author	Razali, Mohd Norhisham Ibrahim, Norizuandi Hanapi, Rozita Mohd Zamri, Norfarahzila Abdul Manaf, Syaifulnizam
author_facet	Razali, Mohd Norhisham Ibrahim, Norizuandi Hanapi, Rozita Mohd Zamri, Norfarahzila Abdul Manaf, Syaifulnizam
author_sort	Razali, Mohd Norhisham
title	Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]
title_short	Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]
title_full	Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]
title_fullStr	Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]
title_full_unstemmed	Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]
title_sort	exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / mohd norhisham razali ... [et al.]
publisher	UiTM Cawangan Perlis
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/86885/1/86885.pdf https://ir.uitm.edu.my/id/eprint/86885/
_version_	1783882329797689344
score	13.211869

Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization / Mohd Norhisham Razali ... [et al.]

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