Dwi Utari Iswavigra
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Data Mining dalam Pengelompokan Penyakit Pasien dengan Metode K-Medoids Dwi Utari Iswavigra; Sarjon Defit; Gunadi Widi Nurcahyo
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.v3i4.150

Abstract

Disease is a condition in which the mind and body experience a kind of disturbance, discomfort for those who experience it. Day by day, the number of patients at the Kuok Health Center is increasing with various types of different diseases. The increase number of patients requires the Kuok Health Center staff always update the patient's medical record data. The patient's medical record data is the form of a report containing the number of patients and their illnesses. Based on these data, the Puskesmas needs to find out information about the diseases that are most vulnerable and suffered by many patients. This study aims to classify patient disease data to find out the most common diseases suffered by patients at the Kuok Health Center, Kampar Regency. The grouping of patient disease data is carried out with the Data Mining Clustering and followed by the K-Medoids method. Next, cluster testing is carried out using the Silhouette Coefficient. The results of this study indicate that in cluster 1 the most common disease suffered by patients is non-insulin dependent diabetes mellitus (type II) with a total of 435 cases. In cluster 2, the most common disease suffered by patients was Essential Hypertension (Primary) with a total of 2785 cases. For cluster 3, the most common disease suffered by patients was Vulnus Laseratum, Punctum, with a total of 328 cases. From the cluster results obtained, the results of the Silhouette Coeficient test are 0.900033674.