Penerapan Metode Association Rule Mining Menggunakan Algoritma ECLAT Untuk Menganalisis Pola Distribusi Obat pada Klinik PMCI

Abstract

Effective medication management is a crucial factor in maintaining stock availability and improving the quality of healthcare services. The PMCI Outpatient Clinic at the Darul Muttaqien Islamic Boarding School (Pesantren Darul Muttaqien) still faces challenges in utilizing historical drug distribution data, potentially leading to stock imbalances. This study aims to analyze drug distribution patterns using Association Rule Mining techniques with the Equivalence Class Transformation (ECLAT) algorithm. The research method employed an exploratory quantitative approach based on one year's worth of drug distribution data. The research stages included data collection and preprocessing, item set formation, application of the ECLAT algorithm, and association pattern analysis based on support and confidence values. The results show that the ECLAT algorithm is capable of identifying drug combinations that are frequently administered simultaneously and exhibit significant relationships. These association patterns can be utilized to support decisions related to stock planning, drug demand prediction, and inventory management optimization in the clinic. These findings provide significant value for improving operational efficiency and helping prevent the risk of drug shortages or overstocks.