Penerapan Algoritma K-Means Clustering Untuk Klasterisasi Penyakit Pasien

Alpazri Lingga

Authors

Alpazri Lingga Universitas Budi Darma, Medan

Keywords:

Data Mining; K-Means; Clustering

Abstract

Rs. Subulussalam City, is the centre of the regional general hospital of Subulussalam city Aceh which is already known and trusted by the local community. Various types of patient diseases that exist in this public hospital, therefore, the purpose of this study is to cluster patients at Rs. Kota Subulussalam, with the problems that are happening in the hospital, namely data retrieval. Subulussalam City, with the problems that are happening in the hospital, namely searching for patient data and also in predicting the level of the number of patients in the Subulussalam city hospital. So that employees and management at the hospital, have difficulty in classifying patient data. Based on the above problems, a patient clustering system was carried out using the K-Means Algorithm. The clustering process is divided into three categories namely self care, intermediate care and total care.With the clustering of patients can determine the number and type of staff needed and determine the value of productivity so that the number of nurses with patient needs can be balanced. By using the K-Means Clustering algorithm, the author will cluster the patient disease data of the subulussalam city public hospital, with the hope that this algorithm can be a solution to the problem so far, so that it will facilitate the parties involved in the development of the clustering that the author compiled. The final result of the application of the K-Means method is that the residents diagnosed with Lung TB are 4 people, while Tbc is 5 people and Hiv Aids is 6 people.

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