Model Artificial Neural Networks (ANN) untuk Prediksi COVID-19 di Indonesia
DOI:
https://doi.org/10.23887/jstundiksha.v12i3.53437Keywords:
ANN (artificial neural network), Backpropagation, Covid-19, Particle Swarm OptimizationAbstract
Peningkatan jumlah kasus Covid-19 hampir terjadi di seluruh dunia penting dikaji karena pola persebarannya yang masif. Metode analisis yang digunakan adalah ANN. Penelitian ini mencakup dua hal, yaitu model ANN algoritma Backpropagation Neural Network (BPNN) dan kombinasi algoritma Backpropagation Neural Network dan Particle Swarm Optimization (BPNN-PSO). Masing-masing model ANN algoritma BPNN dan BPNN-PSO dilakukan 18 kali pengujian. Hasil pengujian menunjukkan kombinasi parameter terbaik ANN dengan algoritma BPNN diperoleh MAPE sebesar 16.29% dengan arsitektur 14-5-1 dengan learning rate 0.3. Kombinasi parameter ANN terbaik algoritma BPNN-PSO MAPE sebesar 15.45% pada arsitektur 14-10-1 dengan learning rate 0,2. Berdasarkan algoritma BPNN dan BPNN-PSO, model ANN terbaik adalah model ANN dengan algoritma BPNN-PSO. Dengan kata lain, nilai MAPE yang cenderung lebih besar dibandingkan MAPE dari hasil algoritma BPNN-PSO. Hal ini mengindikasikan bahwa kombinasi BPNN-PSO berhasil untuk meningkatkan akurasi hasil prediksi. Implementasi algoritma Particle Swarm Optimization (PSO) yang berperan sebagai penentu nilai bobot dan bias awal dalam proses pelatihan metode Backpropagation Neural Network (BPNN) dapat meningkatkan performa model ANN untuk memprediksi jumlah kumulatif kasus positif Covid-19 di Indonesia, dengan peningkatan akurasi yang signifikan. Sehingga penggunaan model ANN dengan algoritma BPNN-PSO dapat diterapkan dalam situasi ini untuk memprediksi jumlah kumulatif kasus positif Covid-19 di Indonesia.
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