Korelasi Kasus Harian Covid-19 dan Pergerakan Saham Perusahaan Vaksin di Pasar Global Menggunakan Long Short-Term Memory (LSTM) - Gigih Setyaji; Kusrini (Universitas Muhammadiyah Riau, 2025)
The COVID-19 outbreak has had a significant impact on stock price fluctuations in the pharmaceutical industry, particularly among vaccine-producing companies. This study evaluates the relationship between the number of daily COVID-19 cases and the stock price movements of global vaccine companies, with a primary focus on AstraZeneca (AZN). The predictive model employed is Long Short-Term Memory (LSTM), a deep learning algorithm based on time series data. To achieve more accurate predictions, automatic hyperparameter tuning was performed using the Optuna method. Based on the evaluation results, the model demonstrated high predictive performance, with a Mean Squared Error (MSE) of 1.131777, Mean Absolute Error (MAE) of 0.773518, Root Mean Squared Error (RMSE) of 1.063850, and a coefficient of determination (R²) of 0.974614. Additionally, the model was able to realistically forecast the AZN stock price trend for the next 30 days. These results prove that the optimized LSTM model can serve as an effective prediction tool for analyzing the impact of the pandemic on the capital market.