## 4 Results  
This section presents the results obtained from applying the proposed AI-driven framework to manage and classify informatics complaints within a public sector environment. The process begins by determining the optimal number of clusters to represent the diverse categories of complaints. Using the Elbow method, the inertia curve was analyzed (Figure 5), and a clear inflection point was observed at four clusters. This indicates that four clusters provide a suitable balance between capturing the complexity of the data and maintaining model interpretability, avoiding overfitting. Subsequently, BERT-based sentence embeddings were generated from the cleaned textual descriptions of complaints. These embeddings were clustered using the K-Means algorithm, and a two-dimensional UMAP projection was employed to visualize the clustering outcome (Figure 6). The UMAP projection shows a visible separation between groups, with some degree of overlap, which is expected given the semantic similarities among certain complaint types. Nonetheless, the overall distribution suggests that the model effectively captures underlying patterns and organizes complaints into coherent operational categories. To assess the robustness of the clustering structure beyond the spherical cluster assumption inherent to K-Means, two additional models were tested: HDBSCAN (a density-based clustering method) and BERTopic (a transformer-based topic modeling framework combining class-based TF-IDF with dimensionality reduction). Both approaches were evaluated using silhouette score and qualitative inspection of cluster coherence. Figure 5: Elbow method analysis for determining the optimal number of clusters. The plot shows the relationship between the number of clusters (K) and the corresponding inertia values. The point of inflection at k = 4 indicates the optimal trade-off between minimizing intra-cluster variance and avoiding overfitting, suggesting the selection of four clusters for subsequent semantic complaint grouping. HDBSCAN yielded fewer, broader clusters with lower silhouette scores (mean = 0.29) compared to K-Means (mean = 0.47), suggesting that the density-based approach was less effective in this context, likely due to uneven density in the embedded space. BERTopic, on the other hand, produced interpretable topic groups but exhibited instability across runs and fragmented semantically similar complaints across multiple topics. While BERTopic was informative for exploratory analysis, its performance in producing operationally actionable clusters was inferior to K-Means, especially when integrated with the dashboard tooling. Given these comparative findings, K-Means was retained for the main clustering pipeline due to its superior quantitative cohesion and alignment with the helpdesk's operational taxonomy. Based on the results, the following 4 clusters emerge, see Table 2. Table 2, presents the four main complaint categories resulting from BERT-based semantic clustering, along with their operational descriptions and representative examples. Each cluster captures a distinct class of issues reported by users, ranging from startup errors and system performance problems to peripheral hardware failures and equipment management needs. Table 3 presents the resulting cluster labels alongside the top 10 most salient keywords per group, offering a semantic summary of the primary issues reported. This labeling process supports downstream tasks such as monitoring issue trends and designing targeted interventions. Building on the clustering results, the framework incorporates a supervised classification model to predict the cluster label of new incoming complaints, enabling automated categorization and real-time decision-making. To automate the semantic categorization of new complaints, a supervised classifier was trained using a Random Forest, SVM, support vector machine (SVM), and XGBoost. The dataset was split 70/30 using stratified sampling, yielding 8990 records for training and 3853 for testing.