A Covid-19 Positive Case Prediction and People Movement Restriction Classification

The world experienced a pandemic that changed people's daily life due to Coronavirus Disease 2019 (covid-19). In Jakarta, the covid-19 cases were discovered on March 18, 2020, and the case increased uncontrollably until the government conducted a movement restriction called pembatasan sosial berskala besar (PSBB). The effectivity of movement restriction was not evaluated in detail. Therefore, we investigated the covid-19 cases in the PSBB period to understand the contribution of movement restriction. Moreover, a prediction model is proposed to computerize the decision of movement restriction. The models are divided into regression and classification models. The regression model is developed to forecast the number of infected cases. At the same time, the classification model is used to identify the best movement restriction type. We utilize data transformation named Principal Component Analysis (PCA) to reduce the number of features. In our case, the best regression method is Multiple Linear Regression (MLP). Then, the best classification method is the Support Vector Machine (SVM). The MLP results are 148.38, 37036.37, and 0.250336 for Mean Absolute Error (MAE), Mean Square Error (MSE), and R2, respectively. In contrast, the SVM achieved an accuracy of 84.81%. Moreover, the prediction system on the website were successfully deployed.

@article{Agastya2022,
  title     = {A Covid-19 Positive Case Prediction and People Movement Restriction Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {8},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {I Made Artha Agastya},
  doi       = {10.14569/IJACSA.2022.0130828},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130828}
}