Implementasi Multipe Linear Regression untuk Prediksi Data Runtun Waktu Pada Penyakit Menular Menggunakan Pendekatan Machine Learning - Wahyuni, Sri Ngudi (JATISI, 2024)
Prediction modeling is one way to get prediction results that are close to their true values. Prediction and machine learning have a relationship in the process-relational approach, where it is used to improve processes, data quality, and model quality. This study aims to implement a Multiple Linear Regression (MLR) model to predict time series data, especially COVID-19 infectious diseases in Indonesia using a Machine Learning approach. This research data was taken from March 2, 2020, to November 8, 2020, and updated by the National Disaster Management Agency (BNPB). The predictive analysis uses parameters of the number of new cases, the number of recovered patients, and the number of deaths. The prediction is carried out over the next 4 days to see the short-term trend of adding new data on COVID-19 patients in Indonesia. The test results show that the R2 value in the MLR model is close to 100%, which is 4.161E+12. So that the Mean Square Error (MAE) value of the MLR model is 1,386E+12 so the MLR accuracy value is 4.1% and the accuracy value is 95.9%.