<![CDATA[Ensuring accurate and analyzable attendance data is essential for managing human resources and discipline in university laboratories. This study analyzes the attendance patterns of 13 laboratory assistants at the Computer Laboratory of FSTT ISTN using a descriptive statistical approach applied to records produced by a GPS-based attendance system with geofencing. The proposed framework, termed the LABKOM Geolocation Attendance Framework (KAGELAB), collects 4,300 attendance records over 485 days and processes them through measures of central tendency and dispersion, tardiness frequency distributions, Pearson correlations among tardiness, early arrival, and overtime, the Jarque–Bera normality test, and a confidence interval for mean tardiness. The results indicate that most attendances are on time, while a smaller subset of high tardiness events generates a right-skewed distribution and a considerable amount of extra time contributed through early arrivals and overtime. Correlations among attendance variables are weak, yet daily, hourly, assistant-level, and monthly analyses reveal consistent patterns of discipline variations. These findings demonstrate that combining a GPS–geofencing attendance system with descriptive statistical analysis provides a robust basis for monitoring discipline and designing data-driven attendance policies in educational laboratory settings.]]>
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