Application of the Levenshtein Algorithm for Optimizing Search Accuracy in a Web-Based Knowledge Management System

This study explores the integration of the Levenshtein algorithm into a web-based Knowledge Management System (KMS) to optimize information retrieval for Academic Service Centers (ASC) in universities. By addressing common challenges such as typographical errors during searches, the Levenshtein algorithm enhances the system's ability to deliver accurate results, improving user experience and service efficiency. The KMS is designed to manage diverse knowledge resources, including procedural guides, tutorials, and documentation, while ensuring accessibility and relevance for students and staff. Testing revealed an 88.2% similarity score in handling string mismatches, demonstrating the system’s effectiveness in managing unstructured academic data. The findings emphasize the value of incorporating fuzzy matching techniques for robust knowledge management, particularly in higher education contexts where accurate information retrieval is critical. This research contributes a scalable framework for implementing KMS using string similarity algorithms, with potential applications extending to broader organizational settings.