Implementasi Metode K-Nearest Neighbor Untuk Mengklasifikasi Jenis Penyakit Katarak
Implementasi Metode K-Nearest Neighbor Untuk Mengklasifikasi Jenis Penyakit Katarak
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实施方法 K-最近邻 Untuk Mengklasifikasi Jenis Penyakit Katarak
DOI：
10.22487/2540766x.2020.v17.i1.15184
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
D. Lusiyanti
中科院分区：
文献类型：
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作者：
M. Safaat;A. Sahari;D. Lusiyanti
眼睛是人类用来欣赏自然之美并与环境正确互动的五种重要感官之一。如果眼睛有问题或疾病，就会很严重。眼部疾病之一是白内障。如果允许白内障，患者的病情会变得更糟。因此，准确判断早期白内障的类型和布局，对于预防更严重的白内障影响是非常重要的。早期找出白内障类型的一种方法是使用数据挖掘的数学方法，即K最近邻(KNN)方法。KNN方法的概念是找到最近的邻居，并选择簇中的大多数类。在这项研究中，该系统根据Anutapura Palu医院白内障患者所经历的症状对白内障类型进行分类，这些患者的研究数据来自2018年1月至2018年3月，总计170个数据。研究结果表明，对于170个数据，KNN方法的准确率为91.76%。关键词：白内障，分类，K-最近邻(KNN)
The eyes is one of the five senses that are very important for humans that are used to see the beauty of nature and interact with the environment properly. If the eyes has a problems or diseases, it will be very severe. One of the disorders in the eye is cataract. Cataract if allowed, it will get worse for the sufferer. Therefore, the accuracy of determining the type and layout of early cataract is very important to prevent the more severe effects of cataract. One way to find out early on the type of cataract is by using the mathematical approach to data mining, namely the K-Nearest Neighbor (KNN) method. The concept of the KNN method is to find the nearest neighbor and choose the majority of the classes in the cluster. In this study, the system classified cataract types based on the symptoms experienced by cataract patients at Anutapura Palu Hospital whose research data was obtained from January 2018-March 2018 which amounted to 170 data. The results of this study indicate the accuracy of the KNN method for 170 data at 91.76% Keywords : Cataract, Classification, K-Nearest Neighbor (KNN)