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Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA )Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA )

Disaster Recovery Centers (DRC) play a crucial role in ensuring the availability and continuity of database operations in enterprise environments. The process of restoring databases from production servers to DRCs is often performed manually, which can lead to errors such as selecting incorrect backups, corrupted files, and lengthy search times. The complexity increases with the growing number of databases and the variety of daily backup types. This study develops an automated system based on a Python Web Interface integrated with Advanced Information Retrieval (IR) to improve the accuracy and speed of finding relevant backups before restoration. The system employs Natural Language Processing (NLP) and multi-criteria relevance scoring, evaluating backup suitability based on fuzzy matching of database names, recency, semantic similarity, backup type, and file size. Testing was conducted using 28 backup records from 5 different databases. Results show that Advanced IR can accelerate backup searches in under 2 seconds, with relevance ranking ranging from 38% to 67%. Additionally, the automated restore process via Python achieved an average execution time of 7.49 seconds with a 100% success rate.

The research successfully developed an automated database restore system to a DRC using a Python Web Interface integrated with Advanced Information Retrieval.The system overcomes issues in manual restoration, such as errors in backup selection and long search times.Through the application of NLP and multi-criteria relevance scoring, the system efficiently identifies and ranks relevant backups, achieving a 100% success rate in restoring five databases with an average time of 7.

Future research should focus on enhancing the systems scoring algorithms using machine learning to adapt to usage patterns and backup characteristics. Integrating real-time backup monitoring would enable predictive restoration recommendations. Expanding platform support to include databases beyond SQL Server, such as PostgreSQL and MySQL, would broaden the systems applicability in heterogeneous enterprise environments. Implementing role-based access control (RBAC) would enhance security and control over system operations. Finally, developing automated restoration validation reporting would provide valuable audit trails and documentation for disaster recovery processes. These advancements will contribute to a more intelligent and robust disaster recovery solution for modern database management.

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