Optimization of the SRIKANDI E-Government System Using XGBoost-Based Classification and One-Class SVM Anomaly DetectionType

Accurate and efficient digital archive management is a crucial component of Electronic-Based Government Systems (SPBE) in Indonesia. The Integrated Dynamic Archival Information System (SRIKANDI), widely used by government agencies, continues to face various challenges such as incomplete metadata, inconsistent classification, and difficulties in archive retrieval and retention scheduling. This study aims to optimize the SRIKANDI system by implementing machine learning algorithms XGBoost for document classification and One-Class SVM (OCSVM) for automatic anomaly detection in metadata. The methodology involves data preprocessing, feature selection, label generation, and the application of classification and anomaly detection models on archival data from the Meteorological, Climatological, and Geophysical Agency (BMKG), Central Java. The XGBoost model achieved a classification accuracy of 77%, showing strong performance in identifying "Destructible" archives but limited ability in detecting the "Permanent" category due to data imbalance. Meanwhile, the OCSVM model successfully identified 16 anomalous entries (9.14%) out of 175 archives, with key indicators including extreme item counts and illogical retention periods. The results demonstrate that integrating machine learning into digital archival systems significantly improves classification accuracy, operational efficiency, and metadata integrity. Furthermore, this approach supports proactive auditing and validation of archival metadata. The findings offer valuable insights for developing AI-powered archival classification and anomaly detection systems to enhance accountability, transparency, and data governance in the public sector.