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Bulletin of Applied Mathematics and Mathematics Education
ISSN : 27761002     EISSN : 27761029     DOI : https://doi.org/10.12928/bamme.v2i1.5129
Core Subject : Education,
BAMME welcomes high-quality manuscripts resulted from a research project in the scope of applied mathematics and mathematics education, which includes, but is not limited to the following topics: Analysis and applied analysis, algebra and applied algebra, logic, geometry, differential equations, dynamical system, fuzzy system, etc. Graph theory, combinatorics, number theory, coding theory, cryptography, etc. Mathematical modeling in economics, physics, biology, medicine, engineering, control theory and automation, optimization, operational research, neural network, data science, machine learning, etc. Applied statistics and probability, finance mathematics, biostatistics, actuary, etc. RME-based mathematics education. Development studies in mathematics education. Mathematics Ability, includes the following abilities: reasoning, connection, communication, representation, and problem solving. Ethnomathematics, the results of research on the relationship between mathematics and culture practiced by members of cultural groups who share experiences and practices similar to mathematics that can be in a unique form. Application of ICT in mathematical learning and the design, development, and evaluation of the implementation or application of learning media.
Articles 1 Documents
Search results for , issue "Vol. 5 No. 2 (2025)" : 1 Documents clear
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.

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