Hybrid Method Implementation Fuzzy K-Nearest Neighbor (FK-NN) And Particle Swarm Optimization (PSO) for Classification of Liver Disease

  • I Gede Bagus Semara Wijaya Universitas Udayana
  • Luh Gede Astuti Udayana University
  • I Putu Gede Hendra Suputra Udayana University
  • I Dewa Made Bayu Atmaja Darmawan
  • I Wayan Santiyasa Udayana University
  • I Gede Santi Astawa Udayana University

Abstract

Liver disease is a disease that attacks the liver or liver where this disease is caused by viral infections, toxic materials and bacteria, causing inflammation of the liver and causing the liver to not function properly. Therefore, the author will conduct research to make a Liver Disease Classification program. This research will use Fuzzy K-Nearest Neighbor (FK-NN) and Particle Swarm Optimization (PSO) methods. Fuzzy K-Nearest Neighbor is a classification method that combines fuzzy and k-nearest neighbor algorithms. Particle Swarm Optimization is a simple optimization technique to apply and modify several parameters. This research will implement the application design to the lines of web-based program code using the Python language and the Django framework. This study resulted in the value of the accuracy range obtained by the PSO-FKNN hybrid method is 66 to 74 (in percent) compared to the range of accuracy values ??of FKNN without the hybrid method is 64.90% to 68.29% (in percent), the difference in the accuracy values ??obtained by PSO-FKNN FKNN is affected by changes in the position of the training and testing data in each test

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Published
2022-07-20
How to Cite
SEMARA WIJAYA, I Gede Bagus et al. Hybrid Method Implementation Fuzzy K-Nearest Neighbor (FK-NN) And Particle Swarm Optimization (PSO) for Classification of Liver Disease. JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), [S.l.], v. 11, n. 3, p. 635-544, july 2022. ISSN 2654-5101. Available at: <https://ojs.unud.ac.id/index.php/jlk/article/view/89357>. Date accessed: 29 mar. 2024. doi: https://doi.org/10.24843/JLK.2023.v11.i03.p20.

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