research-article

The Effect of Mental Workload towards Mental Fatigue on Customer Care Agent Using Electroencephalogram

Authors:

Shafira Karamina Alifah,

Pande Bagus Widyantara,

Maya Arlini PuspasariAuthors Info & Claims

ICIBE '19: Proceedings of the 5th International Conference on Industrial and Business Engineering

Pages 173 - 177

https://doi.org/10.1145/3364335.3364367

Published: 27 September 2019 Publication History

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Abstract

High mental workload can lead to fatigue and further result in decreased concentration and work performance. This study is conducted to see the effects of mental workload towards mental fatigue. Mental fatigue measurement was conducted at the first and the last 10 minutes of the working time using electroencephalogram, while mental workload measurement was conducted after the work is completed using the NASA-TLX questionnaire. The result shows that there is an increase in alpha band which indicates an increase in mental fatigue. This study also shows absolute alpha is more sensitive compared to the relative alpha. This study proves that there is a relationship between mental workload and mental fatigue although not relatively strong.

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Cited By

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Ng GKeikhosrokiani PIsomursu M(2024)Deep-Mental Workload Intelligent SystemData-Driven Business Intelligence Systems for Socio-Technical Organizations10.4018/979-8-3693-1210-0.ch011(268-298)Online publication date: 23-Feb-2024
https://doi.org/10.4018/979-8-3693-1210-0.ch011
Hernández-Sabaté AYauri JFolch PÁlvarez DGil D(2024)EEG Dataset Collection for Mental Workload Predictions in Flight-Deck EnvironmentSensors10.3390/s2404117424:4(1174)Online publication date: 10-Feb-2024
https://doi.org/10.3390/s24041174

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The Effect of Mental Workload towards Mental Fatigue on Customer Care Agent Using Electroencephalogram
1. Social and professional topics
  1. Professional topics
    1. Computing and business
      1. Employment issues

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ICIBE '19: Proceedings of the 5th International Conference on Industrial and Business Engineering

September 2019

398 pages

ISBN:9781450376532

DOI:10.1145/3364335

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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The Hong Kong Polytechnic: The Hong Kong Polytechnic University

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Association for Computing Machinery

New York, NY, United States

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Published: 27 September 2019

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Universitas Indonesia

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ICIBE 2019

ICIBE 2019: 2019 The 5th International Conference on Industrial and Business Engineering

September 27 - 29, 2019

Hong Kong, Hong Kong

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Ng GKeikhosrokiani PIsomursu M(2024)Deep-Mental Workload Intelligent SystemData-Driven Business Intelligence Systems for Socio-Technical Organizations10.4018/979-8-3693-1210-0.ch011(268-298)Online publication date: 23-Feb-2024
https://doi.org/10.4018/979-8-3693-1210-0.ch011
Hernández-Sabaté AYauri JFolch PÁlvarez DGil D(2024)EEG Dataset Collection for Mental Workload Predictions in Flight-Deck EnvironmentSensors10.3390/s2404117424:4(1174)Online publication date: 10-Feb-2024
https://doi.org/10.3390/s24041174

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Recommendations

How Stress and Mental Workload are Connected

Generalizability of Mental Workload Prediction Using VACP Scales in Different Fields

Detection of Cognitive Decline Due to Mental Fatigue Using Electroencephalogram

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