hidayah, Umi Nur (2021) ANALYSIS ON DATA CLASSIFICATION OF PATIENTS WITH HEART FAILURE USING K NEAREST NEIGHBOR METHOD. Undergraduate thesis, UNIVERSITAS MUHAMMADIYAH JEMBER.
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Abstract
This study describes the use of the K Nearest Neighbor method for data classification of patients with heart failure . In this research , several stages and methods were carried out . At the preprocessing stage of this study using the MinMax Scaler method for normalization and the LogX method to reduce the level of skewness and outliers . At stage s k enario test in research is using methods Cross Fold Validation with value k = 3, 5, 7 and 9 and as a differentiator in the research is carried out two processes are different , namely the implementation of the K-NN using techniques SMOTE earlier and implementation K- NN without SMOTE Technique . In measuring the classification results focus on the level of accuracy and precision . In a study of this data is used amounted to 299 patients who come from UCI Machine Learning . Measurement of the distance to the K-NN in the research is to use vector Euclidean Distance with a value of neighborhood 3, 5, 7 and 9. From the implementations are done obtained results that classification K Nearest Neighbor to the data of patients failing heart without SMOTE obtained results validate the best there is on the Nearest Neighbor k = 9 with an average accuracy of 71.59% and accuracy is the highest that is by 83.33% in the fold second . Whereas the highest accuracy test data was obtained at Nearest Neighbor k = 3 with an accuracy value of 71.66% . classification K Nearest Neighbor to the data of patients failing heart by using SMOTE obtained results validate the best there is on the Nearest Neighbor k = 3 with an average accuracy of 87.88% with accuracy the highest that is by 80.14% in the fold sixth and eighth . Whereas in the test data the highest accuracy was obtained at Nearest Neighbor k = 3 with an accuracy value of 63.33%. Keyword: Classification , K-NN, Heart Failure , SMOT E , Cross Fold Validation.
| Item Type: | Thesis (Undergraduate) | |||||||||
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| Subjects: | 000 Computer Science, Information, & General Works > 004 Data Processing, Computer Science | |||||||||
| Divisions: | Faculty of Engineering > Department of Informatics Engineering (S1) | |||||||||
| Department: | S1 Teknik Informatika | |||||||||
| Depositing User: | Umi Nur Hidayah | |||||||||
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| Date Deposited: | 26 Feb 2021 04:39 | |||||||||
| Last Modified: | 26 Feb 2021 07:30 | |||||||||
| URI: | http://repository.unmuhjember.ac.id/id/eprint/8607 |
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