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Analisis Perbandingan Proses Cluster Menggunakan K- Means Clustering dan K-Nearest Neighbor pada Penyakit Diabetes Mellitus
| Content Provider | Semantic Scholar |
|---|---|
| Author | Siringoringo, Ronny Benediktus |
| Copyright Year | 2016 |
| Abstract | Classification is one of the few role of data mining. In the classification function, there are many algorithms that can be used to process input into the desired output, so it must be considered aspects of performance of each algorithm. The purpose of this study was to analyze and compare the performance of K-Nearest Neighbor and KMeans Clustering from the standpoint of accuracy and runing time.Data sets the research came from the UCI Machine Learning Repository, ie: PIMA Indians Diabetes Dataset.Hasil accuracy comparative analysis shows that the value to-accuracy algorithm K-Means Clustering with an accuracy better than 67 143% K-Nearest Neighbor algorithm with 64 286% accuracy in the implementation of the testing process the data sets.sedangkan time K-Nearest Neighbor algorithm is relatively faster than the K-Means Clustering where Watu testing of K-Nearest Neighbor ie 0.2492 seconds while K-Means Clustering is 12.1285 seconds. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://repository.usu.ac.id/bitstream/handle/123456789/55802/Cover.pdf?isAllowed=y&sequence=7 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
