Talent management (TM) necessitates both quantitative and qualitative skills; it is a determining factor in organisational success, including in university institutions. A team member’s position in an organisation was closely related to TM. Artificial intelligence (AI) technology, such as an expert system, has also influenced human resource management. As well as to help human experts in TM, especially for academic staff TM, this study proposed a mobile app expert system called “ASTMES”. This research method was used the certainty factor method with a forward-chaining inference machine, then programmed onto Android-based app with waterfall-SDLC technique. The result shown as ASTMES can categorise talent criteria: insufficient talent (99.7450 %), good talent (99.9942 %), and potential talent (99.9908 %), with a percentage of confidence level, respectively.
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4 April 2024
THE 2ND INTERNATIONAL SEMINAR OF SCIENCE AND TECHNOLOGY
20 October 2022
South Tangerang, Indonesia
Research Article|
April 04 2024
Expert system for academic staff talent management
Y. Yupiter;
Y. Yupiter
a)
1
Department of Management, Jakarta Global University
, Depok, Indonesia
a)Corresponding author: [email protected]
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Mohamad Zaenudin;
Mohamad Zaenudin
2
Department of Mechanical Engineering, Jakarta Global University
, Depok, Indonesia
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Agung Pangestu;
Agung Pangestu
b)
3
Department of Electrical Engineering, Jakarta Global University
, Depok, Indonesia
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Rosyid Ridlo Al Hakim;
Rosyid Ridlo Al Hakim
c)
3
Department of Electrical Engineering, Jakarta Global University
, Depok, Indonesia
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Muhammad Yusro;
Muhammad Yusro
4
Department of Electronic Engineering, State University of Jakarta
, Jakarta, Indonesia
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Yanuar Zulardiansyah Arief;
Yanuar Zulardiansyah Arief
3
Department of Electrical Engineering, Jakarta Global University
, Depok, Indonesia
5
Department of Electrical and Electronic Engineering, Universiti Malaysia Sarawak
, Sarawak, Malaysia
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Ryan Andikawidi Purnama Putra
Ryan Andikawidi Purnama Putra
3
Department of Electrical Engineering, Jakarta Global University
, Depok, Indonesia
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AIP Conf. Proc. 3048, 020027 (2024)
Citation
Y. Yupiter, Mohamad Zaenudin, Agung Pangestu, Rosyid Ridlo Al Hakim, Muhammad Yusro, Yanuar Zulardiansyah Arief, Ryan Andikawidi Purnama Putra; Expert system for academic staff talent management. AIP Conf. Proc. 4 April 2024; 3048 (1): 020027. https://doi.org/10.1063/5.0207212
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