ABSTRACT

In many organizations, including universities, document management remains a predominantly manual process despite advancements in information and communication technology (ICT). This traditional approach often results in inefficiencies, inaccuracies, and time-consuming operations, especially when dealing with large volumes of documents across multiple departments. At St. Paul University Philippines (SPUP), the Document and Data Control Office (DDCO) faces challenges such as tracking, organizing, classifying, and retrieving documents using outdated methods like logbooks and filing cabinets. These issues are exacerbated during audits and accreditations, where timely access to accurate records is critical. To address these challenges, this study proposes the development and implementation of a Records and Documents Management System (RDMS) with data mining techniques. The system aims to automate and streamline document-related processes, including tracking the status and history of documents ensuring compliance with retention policies. By leveraging technology, the RDMS will enhance the efficiency, accuracy, and confidentiality of document management at SPUP. The proposed system will reduce the workload of university staff, minimize errors, and ensure a more reliable tracking mechanism for documents. Furthermore, it will support SPUP’s continuous efforts towards excellence in education and organizational management, aligning with its status as a leading institution in the region. This study highlights the transformative potential of an effective RDMS in improving document handling and supporting institutional operations. Furthermore, IT experts assessed the developed system and was found to be compliant with the software quality standards specifically in terms of functionality, reliability, compatibility, performance efficiency, maintainability, security and portability at a “very great extent.”

Keywords: Record and document management system, software quality standard, Document and Data Control Officer, data mining