ANALYSIS ON DATA CLASSIFICATION OF PATIENTS WITH HEART FAILURE USING K NEAREST NEIGHBOR METHOD

hidayah, Umi Nur (2021) ANALYSIS ON DATA CLASSIFICATION OF PATIENTS WITH HEART FAILURE USING K NEAREST NEIGHBOR METHOD. Undergraduate thesis, UNIVERSITAS MUHAMMADIYAH JEMBER.

[img] Text
a. PENDAHULUAN.pdf
Download (914kB)
[img] Text
b. ABSTRAK.pdf
Download (246kB)
[img] Text
c. BAB I.pdf
Download (261kB)
[img] Text
d. BAB II.pdf
Restricted to Repository staff only
Download (552kB)
[img] Text
e. BAB III.pdf
Restricted to Repository staff only
Download (786kB)
[img] Text
f. BAB IV.pdf
Restricted to Repository staff only
Download (789kB)
[img] Text
g. BAB V.pdf
Restricted to Repository staff only
Download (246kB)
[img] Text
h. Daftar Pustaka.pdf
Download (453kB)
[img] Text
JURNAL ARTIKEL.pdf
Download (624kB)

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)
Subjects: 000 Computer Science, Information, & General Works > 004 Data Processing, Computer Science
Divisions: Faculty of Engineering > Department of Informatics Engineering (S1)
Depositing User: Umi Nur Hidayah
Date Deposited: 26 Feb 2021 04:39
Last Modified: 26 Feb 2021 07:30
URI: http://repository.unmuhjember.ac.id/id/eprint/8607

Actions (login required)

View Item View Item