ANALISIS CLUSTERING TINGKAT KEPARAHAN PENYAKIT PASIEN MENGGUNAKAN ALGORITMA K-MEANS (STUDI KASUS DI PUSKESMAS BANDAR SEIKIJANG) - Mentari Tri Indah Rahmayani (Universitas Pahlawan Tuanku Tambusai, 2018)
Puskesmas Bandar Seikijang is one of the community health center services located in the Bandar Seikijang sub-district of Pelalawan Regency. The number of patients in the puskesmas continues to increase every year. With the increasing number of patients is directly proportional to the increase in the types of patient's disease. To facilitate public health services it is necessary to classify the severity of the patient's disease. Clustering of the patient's disease consists of 3 clusters namely severe disease, moderate disease and mild disease. Disease grouping using the K-Means method. The purpose of this study was to classify the severity of the patient's disease, determine the level of accuracy of the cluster the severity of the patient's disease, find out the most diseases suffered by the community around the health center, and find out the similarities in manual data processing and using Rapid Miner software. Data samples on manual processing were 15 patients and overall data were 278 patients. The results showed the severity of Cluster patient disease (C0) was in severe disease with 47 patients, cluster (C1) was in mild disease with 82 patients, and cluster (C2) was in moderate disease with 149 patients. The severity of the patient's disease is in patients with moderate disease with a percentage of 53.59%. The diseases most often suffered by the community around the puskesmas are ARI, dengue fever and malaria. And the implementation results using Rapid Miner Software are the same as manual data processing.Keywords : Data Mining, Clustering, K-Means, Severity of Patient Disease, Rapid Miner