K-Nearest Neighbor (K-NN) Method for Optimizing Data Training on Diabetes Diagnosis and Chronic

  • Universitas Nusantara PGRI Kediri
  • Program Studi Teknik Informatika Universitas Nusantara PGRI Kediri
https://doi.org/10.25139/inform.v3i2.1042
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Abstract

Information technology has entered various fields, one of which is the health sector. Many researchers develop expert systems, medical records, and hospital registration systems. The diagnostic system is a concern for researchers because, with a diagnosis system, patients can consult through the system without visiting a doctor (expert). To make the diagnosis system,need medical record data from patients who have had previous treatment.The data will be used as a source of diagnostic of system knowledge as data training. Chronic diabetes patient data to be tested are called training data. The K-Nearest Neighbor (K-NN) method is used to detect diabetes and chronic complications. By developing this system, it is expected that the number of patients who have chronic complications and diseases that accompany Diabetes can be suppressed. The results of the K-NN method are very optimal if tested inpatient data training with optimal values of y = 0.73 and x = 0.62

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Published
2018-10-03
How to Cite
, & . (2018). K-Nearest Neighbor (K-NN) Method for Optimizing Data Training on Diabetes Diagnosis and Chronic. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 3(2), 69-73. https://doi.org/10.25139/inform.v3i2.1042
Issue
Vol. 3 No. 2 (2018)
Section
Volume 3 No. 2 2018

Copyright (c) 2018 Risky Aswi Ramadhani, Ratih Kumalasari Niswatin

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