Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce Optimization
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 moral...
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| Format: | Article |
| Language: | English |
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Fakultas Ilmu Komputer UMI
2025-04-01
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| Series: | Ilkom Jurnal Ilmiah |
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| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/2309 |
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| author | Arfiani Nur Khusna Wisdah Efendi Nur Arina Hidayati |
| author_facet | Arfiani Nur Khusna Wisdah Efendi Nur Arina Hidayati |
| author_sort | Arfiani Nur Khusna |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fd47683d714247a98a1f8b6805d503eb |
| institution | DOAJ |
| issn | 2087-1716 2548-7779 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj-art-fd47683d714247a98a1f8b6805d503eb2025-08-20T02:43:46ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792025-04-01171546310.33096/ilkom.v17i1.2309.54-63742Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce OptimizationArfiani Nur Khusna0Wisdah Efendi1Nur Arina Hidayati2Universitas Ahmad DahlanUniversitas Ahmad DahlanUniversitas Ahmad DahlanThis 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.https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/2309attedanceclusteringemployeek-means clusteringsilhoutte coefficient |
| spellingShingle | Arfiani Nur Khusna Wisdah Efendi Nur Arina Hidayati Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce Optimization Ilkom Jurnal Ilmiah attedance clustering employee k-means clustering silhoutte coefficient |
| title | Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce Optimization |
| title_full | Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce Optimization |
| title_fullStr | Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce Optimization |
| title_full_unstemmed | Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce Optimization |
| title_short | Tackling Attendance Analysis: Unraveling Employee Patterns using K-means Clustering for Workforce Optimization |
| title_sort | tackling attendance analysis unraveling employee patterns using k means clustering for workforce optimization |
| topic | attedance clustering employee k-means clustering silhoutte coefficient |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/2309 |
| work_keys_str_mv | AT arfianinurkhusna tacklingattendanceanalysisunravelingemployeepatternsusingkmeansclusteringforworkforceoptimization AT wisdahefendi tacklingattendanceanalysisunravelingemployeepatternsusingkmeansclusteringforworkforceoptimization AT nurarinahidayati tacklingattendanceanalysisunravelingemployeepatternsusingkmeansclusteringforworkforceoptimization |