Supplies and Equipment Inventory, Monitoring and Tracking Management System using Data Mining Techniques
Mignonette B. Tungcul1, Marifel Grace C. Kummer2

1Mignonette B. Tungcul*, Instructor, Scholar of Commission on Higher Education, Department College of Hospitality Management, Cagayan State University, Tuguegarao City, Philippines.
2Marifel Grace Capili-Kummer, Dean, School of Information Technology & Engineering, Doctor in Information Technology Program Coordinator, St. Paul University Philippines.

Manuscript received on June 25, 2021. | Revised Manuscript received on July 01, 2021. | Manuscript published on July 30, 2021. | PP: 81-85 | Volume-10 Issue-2, July 2021. | Retrieval Number: 100.1/ijrte.B61740710221| DOI: 10.35940/ijrte.B6174.0710221
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: In the present time, there are lot of web and software developer who provides different types of databased and online system to ease the burden of the different supply officer’s/inventory officers of different companies and government sectors but Cagayan State University is one of the big universities that remained inventory management in a manual way. This study together with the development of SEIMTMS was conducted to innovate the current system used and to abolish the difficulties and challenges encountered by the Supply Office staffs in inventory management, record keeping, monitoring and tracking, and report generation. Classification and clustering techniques were utilized to produce information and comprehensive decision support reports that aids the Supply Officer and University administration on decision- making and budget allocation. Furthermore, the system used Clustering technique together with MFP algorithm to forecast the frequently purchased supplies and frequently repaired equipment. These decision support reports are essential for Office Heads in identifying items to be purchased for a particular quarter. With the use of ISO IEC 25010:2011 Software Quality Standards, the system was evaluated by IT Experts with a mean 4.67, qualitatively described as “very graet extent”. 
Keywords: Classification Techniques, Clustering Techniques, Data Mining, Inventory Management.