Implementation of a Web-Based Asset Management System with K-Means Analysis for Asset Maintenance Optimization
DOI:
https://doi.org/10.22303/csrid-.17.3.2025.399-407Keywords:
Sistem Inventaris, Berbasis Web, Manajemen Aset, Fakultas Teknik Informatika, UNIVA LabuhanbatuAbstract
Inventory management within academic institutions is often still carried out manually, particularly in the processes of data recording, borrowing, and asset reporting. This manual approach frequently leads to issues such as data duplication, information loss, and delays in reporting. Similar problems occur in the Faculty of Computer Engineering at Universitas Labuhanbatu (UNIVA), where asset and borrowing transactions are still recorded using spreadsheets and physical documents. To address these challenges, this study developed a Web-Based Asset Inventory and Borrowing Management System designed to simplify the digital recording, monitoring, and reporting of assets in an integrated manner. The system was developed using the Waterfall model, which includes stages of requirements analysis, system design, implementation, testing, and evaluation. It was implemented using PHP as the server-side programming language, MySQL as the database management system, and Bootstrap for a responsive user interface. The main features of the system include asset data management, borrowing and returning transactions, inventory search, automated reporting, and asset condition analysis using the K-Means Clustering algorithm. This algorithm is utilized to classify assets based on age, borrowing frequency, duration of use, and physical condition, providing predictive recommendations for maintenance or replacement. Testing results indicate that the system performs well across all functionalities, with efficiency improved by 65%, data accuracy reaching 98%, and user satisfaction measured using the System Usability Scale (SUS) at 83 points, categorized as excellent. This system enhances the effectiveness, efficiency, and accuracy of asset management, while also supporting data-driven decision-making for maintenance and asset planning in higher education environments.
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