Data Mining Menggunakan Algoritma K-Means untuk Klasifikasi Penyakit Infeksi Saluran Pernafasan Akut
DOI:
https://doi.org/10.37034/jsisfotek.v4i1.117Keywords:
Data Mining, K-Means, Cluster, RapidMiner, Acute Respiratory Infection (ISPA)Abstract
Acute Respiratory Infection (ISPA) is. Diseases that can attack one or more parts of the respiratory tract, from the nose (upper tract) to the alveoli (lower tract) including adnexal tissues such as sinuses, middle ear cavity, and pleura. And ISPA is also a type of infectious disease, especially to people who have abnormalities in the immune system, the elderly, and children whose immune systems are not yet fully formed. ISPA is a contagious disease in the world caused by the main cause of morbidity and mortality. And ISPA can reach four million people die every year and 98% of them are caused by lower respiratory tract infections. In Indonesia even ranks first cause of death in children and adults. ISPA in Indonesia ranks first cause of death in children and adults. ISPA also occupies the list of 10 most diseases. To get precise and fast accuracy in classifying the symptoms of ISPA. In this study, there were 250 patient data sourced from the Rahmatan Lil Alamin Clinic. Furthermore, the data is processed using RapidMiner software. Produced 3 clusters, namely cluster C1 (Ordinary ISPA) with 141 members, cluster C2 (Medium ISPA) with 17 members, C3 (severe ISPA) cluster with 7 members. . The test results for cluster 1 get 62 data sets, cluster 2 get 60 data sets and cluster 3 get 43 data sets. because the number of data sets from cluster 3 is smaller, the cluster can be called optimal.
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