<![CDATA[The Indonesian government's Integrity Zone program mandates systematic classification of administrative documents into hierarchical compliance codes, yet manual categorization remains labor-intensive, inconsistent, and unscalable for higher education institutions. This study aims to develop and evaluate an automated multi-label classification pipeline that maps Indonesian bureaucratic documents to hierarchical compliance codes while maintaining computational efficiency for institutional deployment. A curated dataset of 330 Integrity Zone documents from the Faculty of Mathematics and Natural Sciences, Universitas Negeri Medan, annotated across 82 hierarchical codes, was processed using a frozen IndoBERT encoder to extract 768-dimensional contextual embeddings. These features were classified using a balanced One-vs-Rest Logistic Regression model, with decision thresholds optimized via grid search on a held-out validation set to balance precision and recall. The complete pipeline was deployed as a REST microservice integrated into an existing PHP-based document management system. On a held-out test set of 66 documents not used for training, validation, or threshold selection, the pipeline achieved a Macro F1-score of 0.872, Micro F1-score of 0.894, Hamming Loss of 0.082, and a samples-averaged accuracy of 0.917 (pooled label-wise accuracy 0.918; subset accuracy 0.412). The optimized decision threshold of 0.38 favored recall over the conventional 0.50 cutoff, consistent with the higher institutional cost of missing a relevant compliance code. End-to-end inference latency averaged 2.36 seconds on the deployment server. The proposed pipeline shows practical viability as a decision-support tool for resource-constrained public institutions operating under mandatory human verification; the present evaluation is nonetheless limited by a small held-out test set, and several methodological details are reported in full in the Method section to support reproducibility and to rule out validation leakage.]]>
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