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All Journal ILKOM Jurnal Ilmiah Bulletin of Applied Mathematics and Mathematics Education
Hidayati, Nur Arina
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Computer Science & IT Mathematics

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Shopping pattern segmentation: HAC versus K-Means performance analysis Hidayati, Nur Arina; Khasanah, Uswatun
Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v5i2.14502

Abstract

Despite widespread use in consumer analytics, clustering techniques remain underutilized for analyzing household basic food commodity consumption patterns, particularly for developing localized retail strategies and targeted food security policies in resource-constrained contexts. This study addresses this practical gap by systematically comparing Hierarchical Agglomerative Clustering (HAC) and K-Means performance on essential consumption patterns across seven commodities: bread, vegetables, fruit, meat, poultry, milk, and wine. Using dual validation metrics, Silhouette Coefficient and Davies-Bouldin Index, we evaluate clustering effectiveness specifically for small-scale household datasets typical of regional food policy environments. HAC demonstrated superior cluster stability (Silhouette score = 0.2936, DBI = 0.8977) compared to K-Means (0.2912, 0.9871), enabling identification of three actionable consumption segments, namely budget-conscious households with economical protein consumption, high spender households with premium patterns across categories, and balanced/selective households preferring bread and wine. These empirically-derived segments provide implementable frameworks for food subsidy targeting, inventory optimization in local retail contexts, and nutrition intervention program design. The findings demonstrate that methodologically rigorous clustering analysis yields policy-relevant household segmentation even with constrained data, offering practical guidance for evidence-based food security interventions where basic commodity consumption directly informs resource allocation decisions.
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 Efendi, Wisdah Nur Khusna, Arfiani Sugeng Santoso