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INDONESIA
Journal of Information System, Applied, Management, Accounting and Research
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta
ISSN : 25988700     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
JISIMAR (Journal of Information System, Applied, Management, Accounting and Research), terbit empat kali setahun pada bulan Februari, Mei, Agustus dan November, memuat naskah hasil pemikiran dan hasil penelitian di bidang Teknologi Informasi, Sistem Informasi, Sistem Informasi Manajemen, Sistem Informasi Akuntansi, Ilmu Manajemen dan Manajemen Terapan, Manajemen Sumber Daya, Sistem Manajemen Enterprise, Akuntansi.
Arjuna Subject : -
Articles 701 Documents
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PENILAIAN KUANTITATIF KEDISIPLINAN ASISTEN LABORATORIUM FSTT ISTN MENGGUNAKAN SISTEM ABSENSI BERBASIS GEOFENCING DAN GPS Purnomo, Niko; Dinata, Riadi Marta; Marhaeni, Marhaeni; Atmadja, Kurniawan
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 1 (2026): JISAMAR (February 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i1.2320

Abstract

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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