@thesis{thesis,
		author={Sutrisno  Sutrisno},
		title ={Penerapan Particle Swarm Optimization Untuk Meningkatkan Kinerja Algoritma K-Nearest Neighbor Dalam Klasifikasi Penyakit
Diabetes},
		year={2021},
		url={http://repository.universitasbumigora.ac.id/914/},
		abstract={Diabetes is a serious problem that can cause
complications, leading to death. Word Health Organization
(WHO) diabetes killed 1.5 million people in 2012. From these problems
many studies in the field of data mining that apply classification methods
use to diagnose or identify disease
diabetes. This study aims to determine the application and level of accuracy
obtained by the KNN algorithm in the classification of diabetes and
knowing the level of accuracy of the KNN algorithm after being applied by BPSO as a
feature selection.
This research was carried out in stages, namely dataset collection
pima indian diabetes as a test dataset, pre-processing the data using
min-max normalization and BPSO feature selection, dataset sharing using
10-fold cross validation method, classification using the KNN algorithm up to
the testing process to determine the level of accuracy obtained.
The results showed that the classification using the KNN . algorithm
by implementing feature selection using BPSO to obtain the best accuracy
that is equal to 77.214% with the obtained features that affect the pima . dataset
namely Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree
Function, and Age while the classification uses the KNN algorithm without
BPSO feature selection obtained a lower accuracy of 75%. With
Thus the KNN algorithm with BPSO feature selection can be used to
identify diabetes because it has a high level of accuracy
better than the KNN algorithm alone.
Keywords: Diabetes Classification, Particle Swarm Optimization, KNearest Neighbor}
		}