Hidayati Rusnedy
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Identifikasi Tingkat Pemakaian Obat Menggunakan Metode Fuzzy C-Means Hidayati Rusnedy; Gunadi Widi Nurcahyo; S Sumijan
Jurnal Informasi dan Teknologi 2021, Vol. 3, No. 4
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v3i4.152

Abstract

Medicine is one of the irreplaceable components in health services that can help in treating sick people. Planning for drug needs is one of the important aspects in drug management, because it affects the procurement, distribution and use of drugs in health care units. Planning the right drug needs will make procurement effective and efficient so that it is in accordance with the needs of health services with guaranteed quality and can be obtained when needed. Puskesmas is one of the health services that is managed under the District and City Health Offices. However, in reality there are still obstacles in the process of drug procurement at the Puskesmas so that it has not yet achieved excellent service related to the availability of drug services. Clustering in Data Mining can be used to analyze the use of drugs, planning and controlling drugs at the Puskesmas. The method that will be used in this research is the Fuzzy C-Means algorithm, which is the most widely used and relatively successful unsupervised machine learning method among many fuzzy clustering algorithms. The purpose of this study was to categorize drug data which can be used as a reference in making decisions in planning and controlling medical supplies at the puskesmas. Based on 501 Pharmacy Monthly LPLPO data records in October 2020-February 2021, the results obtained in cluster one are 179 types of drugs which are included in the low level of use, cluster 2 there are 18 types of drugs that are included in the moderate level of use and cluster 3 as many as 4 types. drugs that are included in the high level of use.