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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Dicky Suryapranatha
Universitas Islam Indonesia
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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AI-Based Decision Support System for Learning Growth Performance Workforce Use Certainty Factor and Constraint Evaluation Dicky Suryapranatha; Agus Mansur; Imam Djati Widodo
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3719

Abstract

This study presents the design and computational evaluation of an AI-based decision support system for workforce performance in an AI-driven environment under constrained conditions. Previous research has largely emphasized technological determinants and adequate conditions, while neglecting the constraint mechanisms that limit achievable performance. To address this gap, this study integrates Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA) into a unified computational framework. Empirical data from 200 manufacturing workers was used to derive model parameters. The results identified Engagement Level as a key performance driver (β = 0.519), while Self-Regulation Ability emerged as a critical constraint (d = 0.189). These findings were operationalized into a Certainty Factor-based expert system that models performance as a function of positive contribution, negative influence, and constraint thresholds. The proposed model was evaluated using classification metrics, achieving 87% accuracy, 0.85 precision, 0.86 recall, and an F1-score of 0.85. The results demonstrate strong predictive capabilities and confirm that performance is jointly determined by enabling and constraining factors. This study contributes by bridging statistical analysis and AI system design, providing a constraint-aware decision support model for performance evaluation in complex operational environments.
Co-Authors Agus Mansur, Agus Imam Djati Widodo