Implementasi Jaringan CNN-LSTM Untuk Deteksi Citra X-Ray Dada Covid-19
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Abstract
Wabah penyebaran Virus Covid-19 muncul desember 2019 di kota Wuhan, China. Virus tersebut mulai menggemparkan dunia karena begitu cepat menyebar ke seluruh belahan dunia. Virus Covid-19 dapat mampu ditulakan melewati batuk bahkan percikan saat berbicara. Penderita terkena Covid-19 dapat merasakan gangguan pernapasan dan parahnya lagi dapat menyebabkan kematian. Sampai sekarang virus tersebut banyak menyebabkan korban meninggal dunia. Maka dari itu dibutuhkan sistem deteksi otomatis untuk mendiagnosis cepat, agar mencegah penyebaran Covid-19. Penelitian ini mengusulkan sebuah kombinasi metode convolutional neural network (CNN) dan long short-term memory (LSTM) untuk mendeteksi Covid-19 dari citra x-ray dada. Dalam penelitian, CNN digunakan sebagai ekstraksi fitur yang dalam dan LSTM digunakan sebagai deteksi menggunakan fitur yang diekstraksi. Data yang digunakan se- banyak 3.829 citra x-ray dada yang terbagi menjadi 3 kelas yaitu, 1.143 citra x-ray Covid-19, 1.341 citra x-ray Normal dan citra x-ray 1.345 Viral Pneumonia. Dari hasil penelitian menggunakan metode CNN menunjukkan akurasi sebesar 98,7%, presisi 98%, recall 1.00%, spesifisitas 99,6%, dan f1-score 99%. Secara keselu- ruhan, metode CNN-LSTM dapat menjadi salah satu referensi untuk mempreksi penyakit lainnya.
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