Klasifikasi Citra X-Ray Covid-19 Menggunakan Three-layered CNN Model

Authors

  • Aaron Berliano Handoko Universitas Kristen Satya Wacana
  • Ivanna Kristianti Timotius Universitas Kristen Satya Wacana
  • Darmawan Utomo Universitas Kristen Satya Wacana

DOI:

https://doi.org/10.31358/techne.v21i2.316

Keywords:

CNN, COVID-19, klasifikasi, deep neural network, X-ray

Abstract

Tragedi Covid yang melanda dunia perlu mendapat solusi pendeteksian yang cepat untuk mempermudah pengobatannya. Metode tes PCR jumlah alatnya lebih sedikit dibandingkan dengan mesin X-ray di Indonesia. Oleh karena itu, metode pengklasifikasi gambar X-ray dapat digunakan sebagai solusi alternatif.  Pada penelitian ini diusulkan penggunaan model CNN dengan tiga lapisan convolutional dan maxpooling. Dataset image yang digunakan memiliki 1000 image teridentifikasi Covid dan 3000 image sebagai normal. Hyperparameter tuning dilakukan dengan cara membandingkan beberapa kombinasi hyperparameter; learning rate, dropout rate dan density. Model terbaik yang didapatkan adalah model tiga lapisan neural network dengan learning rate = 0,001, density = 64 dan dropout rate = 0,7. Model ini memiliki rata-rata akurasi sebesar 96% dan jumlah parameter sebanyak 7,1% dibandingkan acuan.

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Published

2022-09-29

How to Cite

Handoko, A. B., Timotius, I. K., & Utomo, D. . (2022). Klasifikasi Citra X-Ray Covid-19 Menggunakan Three-layered CNN Model. Techné : Jurnal Ilmiah Elektroteknika, 21(2), 155–168. https://doi.org/10.31358/techne.v21i2.316

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