AOMR: A web-based accreditation online management registry with predictive readiness analytics using TensorFlow
Benzar Glen S. Grepon, Daniel S. Lerongan, Rio Al-Di Dompol & Charry Mae Grepon
Abstract
Accreditation is a critical process in Higher Education Institutions (HEIs) that ensures quality, accountability, and continuous improvement. However, many institutions face challenges in managing accreditation documents due to fragmented storage systems, a lack of centralized monitoring, and limited use of data-driven tools. This study presents the design and development of a web-based Accreditation Online Management Registry (AOMR) integrated with predictive readiness analytics using TensorFlow. The system provides a centralized platform for document management, compliance monitoring, and real-time tracking of accreditation requirements. The predictive module utilizes institutional data, including document completeness, submission timelines, and historical compliance indicators, to estimate accreditation readiness levels. A developmental research design using Agile methodology was employed. The system was evaluated using the ISO/IEC 25010 software quality model by end-users involved in accreditation processes. Results show an overall mean score of 4.48 (Excellent), indicating high levels of usability, functionality, and performance efficiency. The predictive module shows potential to support proactive decision-making, though further validation is recommended. The study contributes a replicable framework that integrates document management and predictive analytics to enhance accreditation readiness in HEIs.
Keywords
accreditation management system, higher education institutions, quality assurance, web-based system, Agile methodology
Author information & Contribution
Benzar Glen S. Grepon. Corresponding author. Master in Information Technology. Supervising Administrative Officer, Northern Bukidnon State College. Email: bgsgrepon@nbsc.edu.ph
Daniel S. Lerongan. Doctor of Philosophy. Quality Assurance Head, Northern Bukidnon State College. Email: dslerongan@nbsc.edu.ph
Rio Al-Di Dompol. Bachelor of Science in Information Technology. ICTMO Staff, Northern Bukidnon State College. Email: raadompol@nbsc.edu.ph
Charry Mae Grepon. Master of Arts in Education. Institute for Teacher Education Faculty, Northern Bukidnon State College. Email: cmcgrepon@nbsc.edu.ph
Authors 1 and 2 conceptualized the study and established the research framework.
Author 1 led the manuscript preparation, including drafting, revision, and the development of the survey questionnaire.
Authors 2 and 4 assisted in the revisions and improvement of the manuscript.
Authors 3 and 4 conducted data analysis and interpretation.
Authors 3 and 1 were responsible for the system design and development, as well as for integrating the Analytics and TensorFlow algorithms.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was not supported by any funding.
Institutional Review Board Statement
Not Applicable
Data and Materials Availability
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
AI Declaration
The author declares the use of Artificial Intelligence (AI) in writing this paper. In particular, the author used ChatGPT and Gemini to improve language clarity, assist with grammar checking, and help organize the literature review. The author takes full responsibility for ensuring proper review and editing of the content generated using AI.
Notes
Acknowledgement
The researchers would like to express their sincere gratitude to all individuals and institutions who contributed to the successful completion of this study. Foremost, the researchers extend their deepest appreciation to the administration of Northern Bukidnon State College for granting permission to conduct the study and for their continued support in advancing research and innovation within the institution. Special thanks are also given to the faculty members and administrative personnel who generously participated in the survey and shared their valuable time and insights, which were essential in generating meaningful findings for this research. The researchers likewise acknowledge the guidance and support of mentors, colleagues, and peers whose expertise and encouragement greatly contributed to the development and completion of this study. Above all, the researchers express their heartfelt gratitude to God Almighty for His wisdom, strength, and guidance throughout the entire research process.
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Cite this article:
Grepon, B.G.S., Lerongan, D.S., Dompol, R.A. & Grepon, C.M. (2026). AOMR: A web-based accreditation online management registry with predictive readiness analytics using TensorFlow. International Journal of Science, Technology, Engineering and Mathematics, 6(2), 22-44. https://doi.org/10.53378/ijstem.353352
License:
This work is licensed under a Creative Commons Attribution (CC BY 4.0) International License.
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