Optimization of the SRIKANDI E-Government System Using XGBoost-Based Classification and One-Class SVM Anomaly DetectionType
DOI:
https://doi.org/10.33005/itij.v3i1.50Keywords:
SRIKANDI, Classification, Archive Anomalies, XGBoost, One-Class SVMAbstract
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.
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[1] A. A. Musaddad, M. Niswah, K. Prasetyo, and S. Hardjati, “Implementasi Manajemen Kearsipan Di Sektor Publik,” Jurnal Governansi, vol. 6, no. 2, pp. 133–143, 2020, doi: 10.30997/jgs.v6i2.2843.
[2] M. Farrell, “Accountability as a mechanism and a virtue in Irish public sector recordkeeping,” Records Management Journal, vol. 34, no. 2–3, pp. 190–204, 2024, doi: 10.1108/RMJ-09-2023-0051.
[3] R. Touray, “A Review of Records Management in Organisations,” OALib, vol. 08, no. 12, pp. 1–23, 2021, doi: 10.4236/oalib.1108107.
[4] H. P. Nur Soulthoni and M. Itasari, “The Implementation of Electronic-Based Archiving to Accelerate Government Digitalization in Indonesia,” Indonesian Journal of Innovation and Applied Sciences (IJIAS), vol. 5, no. 1, pp. 49–57, 2025, doi: 10.47540/ijias.v5i1.1735.
[5] E. U. Janah, P. F. Hidayah, R. N. Adysti, and R. H. Arjuna, “Peran Digitalisasi Arsip untuk Meningkatkan Efektivitas Manajemen Dokumen Arsip di Dinas Arsip Kota Semarang,” Tsaqofah, vol. 5, no. 1, pp. 474–484, 2024, doi: 10.58578/tsaqofah.v5i1.4517.
[6] M. D. Harahap and F. Trimurni, “Kualitas Sistem Informasi Kearsipan Dinamis Terintegrasi (Srikandi) dalam Pelayanan Administrasi di Dinas Perpustakaan dan Arsip Kabupaten Deli Serdang,” Jurnal Penelitian Inovatif, vol. 5, no. 1, pp. 295–312, 2025, doi: 10.54082/jupin.984.
[7] A. Supriyanto and K. Mustofa, “E-gov readiness assessment to determine E-government maturity phase,” Proceeding - 2016 2nd International Conference on Science in Information Technology, ICSITech 2016: Information Science for Green Society and Environment, pp. 270–275, 2017, doi: 10.1109/ICSITech.2016.7852646.
[8] A. Supriyanto, A. Jananto, J. A. Razaq, B. Hartono, and F. Damaryanti, “Alignment of KAMI Index with Global Security Standards in Information Security Risk Maturity Evaluation,” Cybernetics and Information Technologies, vol. 25, no. 2, pp. 173–192, 2025, doi: 10.2478/cait-2025-0018.
[9] M. Suepa, “Pengimplementasian Sistem Informasi Kearsipan Dinamis Terintegrasi (SRIKANDI) Pada Dinas Perpustakaan dan Arsip Daerah Kabupaten Pandeglang,” pp. 304–314, 2024, [Online]. Available: https://conference.ut.ac.id/index.php/semnasip/article/download/3094/1299/8039?TSPD_101_R0=088cbb75e0ab2000b589ab1714292dc9adb5555617e71884101b96b50053e12ab619a4f27036fa6a08149700701430001869109d3ce7d88453a84033dc73310c3ec9c42657b8eaf30d76db28b5eef3c94eda
[10] E. T. Suharmanto and A. Supriyanto, “Assessment Of IDW And ANN On Daily Rainfall Data Imputation in Semarang Central Java,” Sinkron, vol. 9, no. 1, pp. 382–394, 2025, doi: 10.33395/sinkron.v9i1.14452.
[11] Z. Chen, Z. W. Li, J. Huang, S. Z. Liu, and H. X. Long, “An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM,” Scientific Reports, vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-74822-6.
[12] M. Aly and M. H. Behiry, “Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach,” Scientific Reports, vol. 15, no. 1, pp. 1–17, 2025, doi: 10.1038/s41598-025-08436-x.
[13] M. Balega, W. Farag, X. W. Wu, S. Ezekiel, and Z. Good, “Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning,” Electronics (Switzerland), vol. 13, no. 11, 2024, doi: 10.3390/electronics13112148.
[14] S. Suangli, F. Fahmi, and E. M. Zamzami, “Performance Analysis of Support Vector Machine and Xgboost Classifier Algorithms in Predicting Data Heart Disease,” Proceedings - ICT 2023 - 29th International Conference on Telecommunications: Next-Generation Telecommunications for Digital Inclusion and Universal Access, pp. 1–6, 2023, doi: 10.1109/ICT60153.2023.10374048.
[15] K. Yang, S. Kpotufe, and N. Feamster, “An Efficient One-Class SVM for Anomaly Detection in the Internet of Things,” pp. 1–23, 2021, [Online]. Available: http://arxiv.org/abs/2104.11146
[16] S. K. Devineni, S. Kathiriya, and A. Shende, “Machine Learning-Powered Anomaly Detection: Enhancing Data Security and Integrity,” Journal of Artificial Intelligence & Cloud Computing, vol. 2023, no. May 2023, pp. 1–9, 2023, doi: 10.47363/jaicc/2023(2)184.
[17] S. Brokensha, E. Kotze, and B. Senekal, “Machine learning for document classification in an archive of the National Afrikaans Literary Museum and Research Centre,” Sasa Journal, vol. 56, no. 1, pp. 134–147, 2021.
[18] L. Bergman and Y. Hoshen, “Classification-Based Anomaly Detection for General Data,” 8th International Conference on Learning Representations, ICLR 2020, pp. 1–12, 2020.
[19] P. R. Satriawan, G. M. Ferdinand, I. N. P. S. Natha, I. G. A. P. S. D. Sastrawan, N. W. Marti, and N. P. N. P. Dewi, “Evaluasi Dan Perbandingan Algoritma KlasifikasiDalam Analisis Penggunaan Lahan DenganTeknologi Remote Sensing: Sebuah Kajian Sistematik,” INSERT: Information System and Emerging Technology Journal., vol. 5, no. 2, pp. 97–109, 2024.
[20] A. Mehdary, A. Chehri, A. Jakimi, and R. Saadane, “Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection,” Sensors, vol. 24, no. 4, 2024, doi: 10.3390/s24041230.
[21] S. He and Y. Chen, “A high-performance extreme gradient boosting outlier detection framework for integrating the outputs of diverse anomaly detectors for detecting mineralization-related geochemical anomalies,” Journal of Geochemical Exploration, vol. 273, no. February, p. 107741, 2025, doi: 10.1016/j.gexplo.2025.107741.
[22] A. Shilton, S. Rajasegarar, and M. Palaniswami, “Multiclass anomaly detector: The cs++ support vector machine,” Journal of Machine Learning Research, vol. 21, pp. 1–39, 2020.
[23] G. B. N. Rao, N. Kumar, A. Kumar, and P. Raj, “Efficient Intelligent Network Intrusion Detection for SDN Using XGBoost,” 2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024, pp. 1–9, 2024, doi: 10.1109/ICCCNT61001.2024.10723841.
[24] N. Pinon, R. Trombetta, and C. Lartizien, “One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities,” Proceedings of Machine Learning Research, vol. 227, pp. 1783–1797, 2023.
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