Klasifikasi Pengidap Diabetes Pada Perempuan Menggunakan Penggabungan Metode Support Vector Machine dan K-Nearest Neighbour

  • januar adi putra Jurusan Teknik Informatika, Fakultas Teknologi Informasi Institut Teknologi Sepuluh Nopember (ITS) – Surabaya, 60111, Indonesia
  • afrizal laksita akbar Jurusan Teknik Informatika, Fakultas Teknologi Informasi Institut Teknologi Sepuluh Nopember (ITS) – Surabaya, 60111, Indonesia

Abstract

Diabetes Mellitus is a metabolic disease with characteristics of hyperglycemia that occurs due to abnormalities in insulin secretion, insulin action or both. The detection of diabetes mellitus disease  using the dataset Pima Indians had been done by various methods, one of which implementation methods is K-Neaarest Neighbor (KNN). One drawback of the KNN method is the determination of the optimal parameters k. Value of k that are too high will reduce the effect of noise on the classification, but makes the boundaries between each classification is becoming increasingly blurred, while the value of k that is too low will result in sample taking values ​​for the less and lead to reduced accuracy. For this study proposes the use of Support Vector Machine (SVM) as the optimal solution of k determination. In this study, we will implement the hybrid SVM-KNN method to be used as a method of classification of people with diabetes using the dataset "Pima indian". Experiments done by varying the parameter values ​​and the kernel used to see the value of the accuracy of the hybrid SVM-KNN method. Parameters that influence the value of C, tolerance, sigma, bias and the value of k on KNN. The highest average value of the accuracy obtained by using SVM-KNN is 92.00% and proved to be better than traditional SVM method average of the accuracy only 77.60% and KNN is 91%.

Author Biographies

januar adi putra, Jurusan Teknik Informatika, Fakultas Teknologi Informasi Institut Teknologi Sepuluh Nopember (ITS) – Surabaya, 60111, Indonesia

Jurusan Teknik Informatika, Fakultas Teknologi Informasi

Institut Teknologi Sepuluh Nopember (ITS) – Surabaya, 60111, Indonesia

afrizal laksita akbar, Jurusan Teknik Informatika, Fakultas Teknologi Informasi Institut Teknologi Sepuluh Nopember (ITS) – Surabaya, 60111, Indonesia
Jurusan Teknik Informatika, Fakultas Teknologi InformasiInstitut Teknologi Sepuluh Nopember (ITS) – Surabaya, 60111, Indonesia
Published
2016-08-31
How to Cite
PUTRA, januar adi; AKBAR, afrizal laksita. Klasifikasi Pengidap Diabetes Pada Perempuan Menggunakan Penggabungan Metode Support Vector Machine dan K-Nearest Neighbour. INFORMAL: Informatics Journal, [S.l.], v. 1, n. 2, p. 47 - 52, aug. 2016. ISSN 2503-250X. Available at: <https://jurnal.unej.ac.id/index.php/INFORMAL/article/view/2719>. Date accessed: 28 may 2024.

Keywords

Classification, SVM, KNN, Diabetes Millitus, Pima Indian