# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 1 Introduction  
In a rapidly evolving digital landscape, the public sector grapples with critical human resource management challenges, particularly in resource optimization and cohesive control over human capital utilization. Traditional hiring and resource allocation methods, often reliant on subjective assessments and bureaucratic processes, no longer prove effective in dynamic environments. Compounded by a pervasive non-compliance culture and inadequate management systems, these limitations result in inefficiencies, productivitygaps, and a lack of actionable insights for decision-makers. As public sector organizations navigate digital transformation, there is an urgent need for robust, data-driven frameworks to evaluate employee capabilities and optimize workload allocation. Efficiency in public service delivery hinges on an evaluation framework that integrates skill management, competence, training, experience, and diversity in managerial positions. However, the absence of a reliable central management system impedes the collection of statistical data on employee productivity, perpetuating reliance on outdated evaluation systems laden with subjectivity. This gap underscores the necessity for transformative approaches that align with the demands of the digital era. Building on prior work by [PERSON] et al. [1], which proposed an integrated system for assessing employee potential using neuro-fuzzy inference, this study expands the scope by conducting a rigorous comparative analysis of machine learning algorithms. While [PERSON] et al. emphasized skill management and task optimization through quantifiable criteria, their work did not systematically evaluate alternative methodologies. Similarly, [PERSON] et al. [2] highlighted the role of time-based efficiency metrics but left the question of algorithmic superiority in modeling complex workforce dynamics unresolved. To address these gaps, we introduce a comprehensive framework leveraging seven machine learning algorithms: Linear Regression, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient Boosting Machines (GBMs), Bagged Decision Trees, XGBoost, and Support Vector Machines (SVMs). Our methodology, extending the integrated system proposed by [PERSON] et al. [3], emphasizes human-independent efficiency assessment, departing from traditional systems reliant on domain experts. Linear Regression serves as a foundational baseline, elucidating linear relationships between input factors (e.g., work experience, education, age) and employee performance. Subsequent exploration of ANNs captures intricate, non-linear patterns, while ANFIS bridges interpretability and complexity through its hybrid architecture, combining fuzzy logic with neural networks. Ensemble methods such as GBM and XGBoost harness collective decision-making, and SVM maximizes separation margins between performance classes. Through meticulous experimentation, we evaluate these algorithms using metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE), and Huber Loss. ANFIS emerges as the standout performer, demonstrating superior accuracy (54% lower RMSE compared to Linear Regression) and robustness in handling diverse data patterns. Its ability to minimize bias while modeling non-linear relationships positions it as an optimal tool for public sector workforce management. This study makes three key contributions:  
* A systematic comparative analysis of machine learning algorithms tailored to public sector constraints, addressing the lack of empirical benchmarks in bureaucratic environments. * Validation of ANFIS's superiority in accuracy and interpretability, achieving a significant lower RMSE compared to all other algorithms, as detailed in Section 4. * Practical insights for integrating ANFIS into operational systems, enabling real-time workload management and resource allocation.