Klasifikasi COVID-19 menggunakan Filter Gabor dan CNN dengan Hyperparameter Tuning

AGUS EKO MINARNO, MOCHAMMAD HAZMI COKRO MANDIRI, MUHAMMAD RIFAL ALFARIZY

Sari


ABSTRAK

Penyakit COVID-19 dapat timbul karena berbagai faktor sebab dan akibat, sehingga penyakit ini memiliki efek buruk bagi penderita. Pencitraan CT-Scan memiliki keunggulan dalam memproyeksikan kondisi paru-paru pasien penderita, sehingga dapat membantu dalam mendeteksi tingkat keparahan penyakit. Dalam studi ini, penelitian dilakukan untuk mendeteksi penyakit COVID-19 melalui citra CT-Scan menggunakan metode Filter Gabor dan Convolutional Neural Networks (CNN) dengan Hyperparameter Tuning. Data yang digunakan yaitu citra CT-Scan SARSCoV-2 berjumlah 2481 gambar. Sebelum melatih model, dilakukan preprocessing data, seperti pelabelan, pengubahan ukuran, dan augmentasi gambar. Pengujian Model dilakukan dengan beberapa skenario uji. Hasil terbaik diperoleh pada skenario untuk model Filter Gabor dan CNN dengan Hyperparameter Tuning mendapatkan akurasi sebesar 97,9% dan AUC sebesar 99% dibandingkan dengan model tanpa Hyperparameter Tuning dan Filter Gabor.

Kata kunci: COVID-19, CNN, Filter Gabor, Hyperparameter Tuning, COVID-19 Classification

 

ABSTRACT

COVID-19 disease can arise due to various causal and causal factors, so it has an adverse effect on patients. CT-Scan imaging has an advantage in projecting the lung condition of patients with the patient, so it can help in detecting the severity of the disease. In this study, research was conducted to detect COVID-19 disease through CT-Scan imagery using Gabor Filter method and Convolutional Neural Networks (CNN) with Hyperparameter Tuning. The data used is CT-Scan SARSCoV-2 imagery amounting to 2481 images. Before training the model, preprocessing data is performed, such as labeling, resizing, and augmentation of images. Model testing is performed with multiple test scenarios. The best results were obtained in scenarios for The Gabor Filter model and CNN with Hyperparameter Tuning getting 97.9% accuracy and AUC by 99% compared to models without Hyperparameter Tuning and Gabor Filter.

Keywords: COVID-19, CNN, Filter Gabor, Hyperparameter Tuning, COVID-19 Classification


Kata Kunci


COVID-19; CNN; Filter Gabor; Hyperparameter Tuning; COVID-19 Classification

Teks Lengkap:

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v9i3.493

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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

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