Pengembangan Model Rekomendasi untuk Mendukung Keputusan Human Resource Berbasis Analisis Pola Kehadiran Pegawai dengan Pendekatan Data Analytics

Bryan Agam Kottama; Baskoro Adi Pratomo; Feby Artwodini Muqtadiroh

doi:10.55606/jimas.v5i3.2862

Authors

Bryan Agam Kottama Institut Teknologi Sepuluh Nopember
Baskoro Adi Pratomo Institut Teknologi Sepuluh Nopember
Feby Artwodini Muqtadiroh Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.55606/jimas.v5i3.2862

Keywords:

Clustering, DBSCAN, Decision Support System, Employee Attendance, Human Resource Analytics

Abstract

Employee attendance is an important indicator reflecting discipline and work commitment in public sector organizations. However, attendance data in higher education institutions is still largely utilized for administrative purposes and has not been fully transformed into information that supports human resource (HR) decision-making. This study aims to develop a recommendation model to support HR decisions based on employee attendance pattern analysis using a data analytics approach. A quantitative exploratory approach was applied using electronic attendance data from 2,151 active employees at a public university during the 2024–2025 period. The data were processed into 54,543 monthly behavioral scorecard observations through preprocessing, feature engineering, exploratory data analysis, K-Means clustering, DBSCAN-based anomaly detection, and model evaluation. The results indicate differences in attendance behavior patterns between lecturers and educational staff. Lecturers were dominated by low punctuality patterns, while educational staff were predominantly categorized as highly disciplined employees. DBSCAN complemented the segmentation process by detecting periodic behavioral deviations aligned with HR risk indicators. The developed DSS dashboard integrates behavioral scorecards, clustering results, anomaly detection, and risk prioritization to support evidence-based HR decision-making.

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