Usability Evaluation of Workforce Planning System Using the System Usability Scale (SUS)
- DOI
- 10.2991/978-94-6463-982-7_16How to use a DOI?
- Keywords
- Workforce Planning System; System Usability Scale (SUS); Usability Evaluation; Artificial Intelligence; Human–Computer Interaction; User Experience; Labor Market Analysis
- Abstract
This study evaluates the usability of Talent Cerdas, an AI-powered workforce planning platform designed to address skill-job mis- matches in Indonesia’s Riau Islands Province. Unlike traditional job portals, this system employs machine learning algorithms for intelligent job matching and real-time labor market analytics. Using the System Usability Scale (SUS) methodology, we assessed the platform with 218 job seekers during a regional job fair. The evaluation yielded a mean SUS score of 79.44, exceeding the industry benchmark and indicating good to excellent usability. Subgroup analyses revealed significant differences based on device type and user experience levels. Key usability challenges identified include mobile responsiveness and navigation complexity. Findings reveal usability strengths and developmental priorities that inform future system iterations.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Miratul Khusna Mufida AU - Metta Santiputri AU - Sartikha AU - Banu Failasuf AU - Fauziah Muchlis AU - Evaliata Br Sembiring PY - 2025 DA - 2025/12/29 TI - Usability Evaluation of Workforce Planning System Using the System Usability Scale (SUS) BT - Proceedings of the 8th International Conference on Applied Engineering (ICAE 2025) PB - Atlantis Press SP - 264 EP - 280 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-982-7_16 DO - 10.2991/978-94-6463-982-7_16 ID - Mufida2025 ER -