Implementasi Algoritma Apriori untuk Mengidentifikasi Pola Asosiasi Peresepan Obat dan Penyakit di Klinik Pratama Az-Zainiyah

DOI: https://doi.org/10.33650/trilogi.v6i2.11573

Authors (s)


(1) * Cahyuni Novia   (Universitas Nurul Jadid)  
        Indonesia
(2)  Nurul Imaniyah   (Universitas Nurul Jadid)  
        Indonesia
(3)  Sudriyanto Sudriyanto   (Universitas Nurul Jadid)  
        Indonesia
(*) Corresponding Author

Abstract


The Az-Zainiyah Primary Clinic under the auspices of the Nurul Jadid Islamic Boarding School provides health services to students and the surrounding community. Daily drug prescribing activities generate historical data that has not been optimally utilized. This study aims to identify patterns of association between drug prescribing and common diseases by applying the Apriori algorithm as a data mining method. The data used came from 3,661 student prescription transactions during February 2023. The results of the analysis revealed a combination of drugs that were often prescribed together, including Scopma Plus and Coro-sorb, with a support value of 0.062, confidence of 0.625, and lift of 4.068. This combination was strongly correlated with complaints of diarrhea and abdominal pain, which were also common findings during field observations. These findings have practical implications in the formulation of drug stock policies, increasing service efficiency, and data-based clinical decision making in the clinic environment.

Keywords

Apriori Algorithm; Data Mining; Drug-Disease Association; Health Clinic; Prescribing Pattern.



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