DEEP NEURAL NETWORK UNTUK PREDIKSI INFEKSI COVID-19 BERDASARKAN DIAGNOSIS GEJALA

DEEP NEURAL NETWORK UNTUK PREDIKSI INFEKSI COVID-19 BERDASARKAN DIAGNOSIS GEJALA

• By Muhammad Iqbal;
• Tesis
• 258
• 1-68

Set data individu yang diuji untuk COVID-19 dari Kementerian Kesehatan Israel terdiri dari delapan fitur biner yaitu jenis kelamin, usia 60 tahun ke atas, diketahui kontak dengan individu yang terinfeksi dan lima gejala klinis awal seperti batuk, demam, sakit tenggorokan, sesak nafas dan sakit kepala. Tujuan dari penelitian ini adalah untuk mengembangkan model yang dapat memprediksi apakah seorang pasien terinfeksi COVID-19 berdasarkan gejala klinis yang memungkinkan dapat membantu paramedis dalam skrining awal pasien COVID-19 secara efektif dan mengurangi beban sumber daya medis yang tersedia dirumah sakit. Diusulkan prediksi infeksi COVID-19 dengan menggunakan salah satu model pembelajaran
mendalam adalah Deep Neural Network (DNN) dengan berdasarkan diagnosis gejala seperti jenis kelamin, usia 60 ke atas, indikasi tes dan lima gejala klinis awal. Keefektifan model DNN dapat membantu menyempurnakan parameter dengan algoritma back propagation. Hasil penelitian menggunakan teknik sampling dengan metode teknik oversampling SMOTE untuk prediksi infeksi COVID-19 dari kelas negative dan positive, parameter dengan hidden layer 4, learning rate 0.001 dan optimasi SGD menghasilkan model tertinggi dengan nilai
akurasi 96.23%, AUC 93.10% dan Precision-Recall 77.03%.

• Katakunci
• URL http://
• Penerbit ,

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