Deteksi Covid-19 Menggunakan Citra X-Ray Metode Gray Level Co-Occurrence Matrix (GLCM) dan Adaptive Neuro Fuzzy Inference System (ANFIS)

Authors

  • Fitri Mellynia Astiti Universitas Islam Negeri Sunan Ampel Surabaya
  • Noormann Atoillah Universitas Islam Negeri Sunan Ampel Surabaya
  • Rahmat Rizki Sinulingga Universitas Islam Negeri Sunan Ampel Surabaya
  • Achmad Room Fitrianto Universitas Islam Negeri Sunan Ampel Surabaya

Keywords:

Adaptive Neuro Fuzzy Inference System, COVID-19, Gray-Level Co-Occurrence Matrix

Abstract

The first detected COVID-19 was in China, this virus has spread worldwide rapidly. COVID-19 is caused by the SARS-CoV-2 virus (Severe Acute Respiratory Syndrome Corona Virus-2) or an acute infection that attacks the respiratory system. COVID-19 examination can be carried out by X-rays. The X-ray images will be identified using a CAD system or Computer-Aided Diagnosis. CAD has three processes consisting of preprocessing, feature extraction, and classification. This study compares 200 X-ray image data of COVID-19 data and 200 X-ray image data of non-COVID-19 data. Both groups of data were divided using the K-fold Cross Validation method where the K value used is 10 so that the distribution of training data is 90% and testing is 10%. The epoch used is 5 with 4 parameters (contrast, correlation, energy, and homogeneity). In the feature extraction process, GLCM is used by comparing every angle in the feature extraction, while Adaptive Neuro Fuzzy Inference System (ANFIS) is used for classification. The best results were obtained from GLCM at an angle of 0° with an accuracy value of 0.90, a sensitivity of 0.85 and a specificity of 0.95. This shows that the use of GLCM and ANFIS for COVID-19 detection performs well.

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https://www.kaggle.com/c/siim-covid19-detection

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Published

2023-06-05

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Vol. 7 No. 2 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika

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