Expert system for academic staff talent management.

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. [ABSTRACT FROM AUTHOR]