Classification of Personal Account Identification via Mobile NFC Devices Using the K-Means Algorithm
DOI:
https://doi.org/10.29103/sisfo.v10i2.27751Keywords:
Near Field Communication (NFC), Gym Membership, Automated AttendanceAbstract
The use of Near Field Communication (NFC) in membership systems has commonly been limited to automated identification, attendance recording, or access verification, whereas K-Means clustering studies generally analyze stored datasets without direct integration with real-time operational data acquisition. This separation limits the ability of fitness center systems to transform attendance records into behavioral information for managerial use. This study proposes Gymku, a mobile-based gym membership system that integrates NFC-based attendance acquisition with K-Means-based member segmentation. The main objective is to evaluate whether attendance and membership data collected through an NFC-enabled mobile system can be processed into meaningful member segments. The system was developed using the Waterfall Software Development Life Cycle, covering requirement analysis, system design, implementation, and evaluation. The clustering process used 700 customer records obtained from membership and transaction data, consisting of attendance frequency, membership status, customer type, package type, payment method, and transaction-related attributes. Data preprocessing was conducted through attribute selection and categorical-to-numerical transformation before applying the K-Means algorithm within the Knowledge Discovery in Databases framework. The system evaluation included NFC processing performance, database response time, application performance, clustering validation, and functional access-control testing. The NFC-based attendance process achieved an average processing latency of 0.256 seconds, with average database storage and retrieval times of 0.47 seconds and 0.25 seconds, respectively. The K-Means model produced three member segments representing active, inactive, and pending membership patterns, with a Davies-Bouldin Index of 0.874 and a Silhouette Score of 0.856, indicating relatively compact and well-separated clusters. Cluster-label mapping against reference categories resulted in 81.81% accuracy. These findings show that integrating NFC and K-Means can extend a gym membership system from simple digital attendance recording into operational member segmentation. However, the segmentation results remain specific to the dataset used in this study, and broader validation across different fitness center environments is still required.
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