PERANCANGAN KLASIFIKASI PASIEN STROKE DENGAN METODE K-NEAREST NEIGHBOR

Authors

  • Rahmat Ardila Dwi Yulianto Universitas Ahmad Dahlan
  • Imam Riadi Universitas Ahmad Dahlan
  • Rusydi Umar Universitas Ahmad Dahlan

DOI:

https://doi.org/10.36341/rabit.v8i2.3454

Keywords:

Classsication, K-NN, Sroke, UML

Abstract

Stroke is a disease characterized by impaired brain function caused by a lack of oxygen supply and blood flow to the brain, affecting several brain functions that make sufferers experience difficulty in carrying out activities. the classification of stroke patients found is still in the form of medical records that have not been integrated so it takes longer time to detect. The K-NN algorithm is part of a machine learning algorithm that can be used to classify one of the cases, namely the classification of stroke patients. K-NN is used as a class determining algorithm to enter new data that is input according to the format. Based on the results obtained, this study leads to system design using the Unified Model Language (UML) and system user interface design.

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Published

2023-09-11

How to Cite

[1]
R. A. D. Yulianto, I. Riadi, and R. Umar, “PERANCANGAN KLASIFIKASI PASIEN STROKE DENGAN METODE K-NEAREST NEIGHBOR”, rabit, vol. 8, no. 2, pp. 262–268, Sep. 2023.

Issue

Vol. 8 No. 2 (2023): Juli

Section

Articles

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