Modelling perspective on the number of confirmed cases of COVID-19

The increase in the number of confirmed cases of COVID-19 in various places varies widely. There are differences in data patterns from one region to another, both in number and duration. In one area, the epidemic may seem to have ended however, in other areas it has entered a second wave. Other regions have entered the third phase although with smaller waves. The pattern between the waves is also different. Since the beginning of the pandemic, various models and their development have been tried to be applied to make predictions in various locations. However, a precise prediction of the end of the pandemic has not been able to be done, at least until the current period. This study makes short-term and long-term predictions using nonlinear model approaches. The data used is the number of confirmed cases in Indonesia. Neural network model with Extreme Machine Learning algorithm able to predict accurately for short-term predictions. The long-term predictions have also been developed using the Richards model. However, their accuracy has not yet been determined. This can be concluded after the pandemic has ended. Nevertheless, predictions are still important to do for strategic planning purposes. © 2023 Author(s).

Accuration of Classification of Covid with Convolutional Neural Network-Based Image Chest X-ray with Variations in Image Size and Batch Size

The use of convolutional neural networks in Covid classification has a positive impact on the speed of justification and can provide high accuracy. But on the one hand, the many parameters on CNN will also have an impact on the resulting accuracy. CNN requires time and a heavy level of computation. Setting the right parameters will provide high accuracy. This study examines the performance of CNN with variations in image size and minibatch. Parameter settings used are max epoch values of 100, minibatch variations of 32, 64, and 128, and learning rate of 0.1 with image size inputs of 50,100, and 150 variations on the level of accuracy. The dataset consists of training data and test data, 200 images, which are divided into two categories of normal and abnormal images (Covid). The results showed an accuracy with the use of minibatch 128 with the highest level of accuracy at image size 150 × 150 on test data of 99,08%. The size of the input matrix does not always have an impact on increasing the level of accuracy, especially on the minibatch 32. The parameter setting on CNN was dependent on the CNN architecture, the dataset used, and the size of the dataset. One can imply that optimization parameter in CNN can approve good accuration. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Post-Covid-19 Pandemic Education Model Is Effective, Let's Compare: Online Versus Offline Learning

Many variations of learning methods, ranging from discussions, dialogues, to simulations. When the COVID-19 pandemic began to emerge, learning models using information technology began to become a trend. Many media can be used as learning media such as the internet, mobile phones, and other platforms. The problem is that not all teachers and students understand the use of information technology. The solution offered is to create a model with the 3M concept (Methods, Media, and Materials) which will be the basis for educators and students to create balance and ease in the learning process. Method: To process the existing data, the identification concept will be used which is then made a hierarchical arrangement to facilitate the analysis of the results. The results were obtained in the form of several parameters that can be used as the basic for designing the 3M model (Method, Media, and Material).. © 2021 IEEE.