OPTIMIZING PHARMACEUTICAL DISTRIBUTION IN PUBLIC HEALTH CENTERS USING FUZZY C-MEANS CLUSTERING
DOI:
https://doi.org/10.33480/techno.v20i2.7336Keywords:
clustering, drug distribution , fuzzy c-means , healthcare logistics , public healthAbstract
Efficient drug distribution is fundamental to ensuring the quality of public healthcare services. However, health departments often face challenges with imbalances between drug demand and available supply. This study addresses this issue by applying the Fuzzy C-Means (FCM) clustering algorithm to categorize drug demand levels across 16 public health centers (puskesmas) in Langkat Regency, Indonesia, from 2021 to 2023. Using historical data from 2,400 drug records, the analysis identified five distinct demand clusters: Very Low, Low, Medium, High, and Very High. The results revealed a significant disparity in drug needs, with the "Very High" demand cluster dominating (51.29% of data) in centers like Besitang and Tanjung Selamat, driven by high morbidity rates. In contrast, other clusters were less prevalent, such as the "Low" demand cluster, which was primarily concentrated in the Gebang health center. These findings, visualized using t-SNE plots, highlight significant regional variations in pharmaceutical needs. This data-driven clustering provides a robust framework for the Langkat District Health Office to develop more targeted, efficient, and equitable drug distribution strategies, ultimately improving healthcare service delivery.
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