Classification of Stroke Opportunities with Neural Network and K-Nearest Neighbor Approaches

Authors

  • Nurul Afifah Arifuddin Univeristy Pembangunan Nasional Veteran Jakarta, Indonesia
  • I Wayan Rangga Pinastawa Univeristy Pembangunan Nasional Veteran Jakarta, Indonesia
  • Nurhajar Anugraha Polytechnic Sriwijaya
  • Musthofa Galih Pradana Univeristy Pembangunan Nasional Veteran Jakarta, Indonesia

DOI:

10.33395/sinkron.v8i2.12228

Keywords:

Classification, K-Nearest Neighbor, Multi-Layer Perceptron, Stroke

Abstract

Stroke is one of the deadly diseases. This is illustrated in stroke
deaths in Indonesia which reached a death rate of 131.8 cases. Some of the
things that cause a stroke to become a disease with the highest mortality rate
are related to transitions in human life in 4 aspects, namely epidemiology,
demography, technology, and economics, socio-culture. Of the many
influencing aspects, one of the transition points of human life in the
technological aspect can be an alternative solution and prevention. Aspects
of technology with the utilization of data can be used as a preventive measure
for stroke. One approach is to use data mining techniques, which can provide
an initial picture regarding the chances of getting a stroke so that it can be
used as an early warning for patients. With so many techniques in data
mining, this study used a classification or grouping approach using 2
algorithms, namely K-Nearest Neighbor and one of the Neural Network
groups, namely Multi-Layer Perceptron. This research will focus on finding
the accuracy and best results of the two algorithms in classifying. The final
result of this study is that the K-Nearest Neighbor algorithm has a better
accuracy of 95% compared to the Multi-Layer Perceptron which produces an
accuracy of 88%

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How to Cite

Arifuddin, N. A., Pinastawa, I. W. R. ., Anugraha, . N. ., & Pradana, M. G. . (2023). Classification of Stroke Opportunities with Neural Network and K-Nearest Neighbor Approaches. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(2), 688-693. https://doi.org/10.33395/sinkron.v8i2.12228

Issue

Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023

Section

Articles

License

Copyright (c) 2023 Nurul Afifah Arifiuddin, I Wayan Rangga Pinastawa, Nurhajar Anugraha, Musthofa Galih Pradana

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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