Prediksi Penyebaran COVID-19 Di Indonesia Menggunakan Long Short Term Memory
Keywords:
COVID-19, Indonesia, LSTM, MSE, PredictionAbstract
Penelitian ini bertujuan untuk memprediksi arah tren penyebaran COVID-19 di Indonesia, serta memperkirakan jumlah kumulatif kasus terkonfirmasi dan kematian dalam 30 hari ke depan. Model Long Short-Term Memory (LSTM) digunakan karena kemampuannya dalam mengolah data time-series, sementara Mean Squared Error (MSE) digunakan sebagai fungsi kerugian untuk mengukur akurasi prediksi. Sebanyak sembilan skenario pengujian dilakukan untuk masing-masing kategori kasus terkonfirmasi dan kematian, guna menentukan model terbaik. Berdasarkan hasil pengujian, model menunjukkan arah tren yang meningkat (uptrend) untuk kasus kumulatif terkonfirmasi maupun kematian. Prediksi menunjukkan bahwa pada tanggal 19 Juli 2021, jumlah kasus terkonfirmasi di Indonesia akan mencapai 2.917.557 kasus. Sementara itu, prediksi untuk jumlah kasus kematian hingga 9 Agustus 2021 diperkirakan mencapai 73.265 kasus. Temuan ini diharapkan dapat menjadi acuan bagi pemangku kebijakan dalam menyusun strategi penanganan pandemi secara lebih tepat dan terukur.
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