K-Means Clustering for Drug Inventory Analysis at Anugrah Pharmacy Bekasi
DOI:
https://doi.org/10.69916/jkbti.v5i3.577Keywords:
Data Mining, CRISP-DM, K-Means Clustering, Drug Inventory, Sales PatternAbstract
Improper drug inventory management can cause stockouts, overstocking, and
inefficient procurement decisions, especially in small-scale retail pharmacies
that still rely on manual estimation. This study analyzes drug sales patterns
and groups drug inventory at Anugrah Pharmacy Bekasi using the K-Means
clustering algorithm within the Cross Industry Standard Process for Data
Mining (CRISP-DM) framework. The dataset consisted of 5,312 sales transactions
from January to June 2025. The transaction records were aggregated
into 731 drug items using three variables: transaction frequency, sales volume,
and transaction value. Data preparation included aggregation, missing-value
checking, duplicate checking, transformation, and Min-Max normalization.
The optimal number of clusters was determined using the Elbow Method,
which indicated three clusters (k = 3). The K-Means results grouped the 731
drug items into 28 Fast Moving items (3.83%), 129 Medium Moving items
(17.65%), and 574 Slow Moving items (78.52%). The centroid analysis shows
that each cluster has distinct sales-movement characteristics. The results can
support inventory decision-making by helping the pharmacy prioritize replenishment
for fast-moving drugs, maintain controlled stock for medium-moving
drugs, and limit excessive procurement for slow-moving drugs. This study
demonstrates that CRISP-DM and K-Means clustering can provide practical
information for data-driven drug inventory management in a retail pharmacy
context.
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Copyright (c) 2026 Alya Prciscilla putri, Adi Muhajirin, Fata Nidaul Khasanah
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