Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal ILKOM Jurnal Ilmiah
Efendi, Wisdah
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce Optimization Nur Khusna, Arfiani; Efendi, Wisdah; Hidayati, Nur Arina
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2309.54-63

Abstract

This study aims to apply the K-Means Clustering method using employee attendance data. The background of this research problem is to improve the understanding and management of employee attendance by identifying similar attendance patterns in different groups. Employee attendance impacts their morale, sense of responsibility, discipline, cooperation with supervisors or colleagues, and their level of productivity. The K-means Clustering method divides employees into groups based on their attendance patterns, to create groups with similar attendance characteristics. This research has important benefits in decision-making related to human resource management, scheduling, and employee performance evaluation. The results of the study were measured using the Silhouette Coefficient, with a value of 0.3140272065284342, which shows a moderate level of accuracy in separating groups based on attendance patterns. Furthermore, the study also achieved a 100% truth value, signifying the success of consistent and accurate grouping. The main contribution of this research is the use of the K-Means Clustering method as an effective tool in analyzing the attendance of employees and providing valuable insights into managing employee attendance by understanding existing attendance patterns.
Co-Authors Hidayati, Nur Arina Nur Khusna, Arfiani