COVID-19 detection through radiological examination is favoured since it is fast and produces more accurate results than the laboratory approach. However, when it has infected many people and put a strain on the healthcare system, the need for fast, automatic COVID-19 detection in patients has become critical. This study proposes to detect COVID-19 from chest X-ray (CXR) images with a machine learning approach. The main contributions of this paper are to compare two powerful deep learning models, i.e., convolutional neural networks (CNN) and the combination of CNN and Long Short-Term Memory (LSTM). In the combination model, CNN is recommended for feature extraction, and COVID-19 is classified using the features of LSTM. The dataset used in this study amounted to 4,095 CXR images, consisting of 1,400 images of normal conditions, 1,350 images of COVID-19, and 1,345 images of pneumonia. Both CNN and CNN-LSTM were executed in a similar experimental setup and evaluated using a confusion matrix. The experiment results provide evidence that the CNN-LTSM is better than the CNN deep learning model, with an overall accuracy of about 98.78%. Furthermore, it has a precision and recall of 99% and 98%, respectively. These findings will be valuable in the fast and accurate detection of COVID-19.

As time passes, social media, which was formerly used as a means of communication between users, is experiencing a transition as a means for broadcasting information, conducting business, advertising, and even political campaigning. In elections, social media is also used to discredit political opponents to reduce the electability of opposing candidate. Spreading hate speech and fake news to undermine the electability of opposing candidate is a common violation of the law committed by supporters of one candidate over another. Considering that the number of social media users increases annually at a very rapid rate, the hazard of social media abuse has the potential to grow. In 2022, Indonesia had 191 million social media users in January 2022. Obviously, this will make the election situation more tumultuous and has the potential to cause societal divisions. The government must have a control system in place to screen social media content that can be considered illegal. In this study, fake news and hate speech are classified using the Bidirectional Gated Recurrent Unit (BiGRU). Lastly, K-Prototype was used to do clustering based on categorization dimensions and probable distribution to identify which clusters had the greatest risk of breaking the law, creating confusion, and dispersing broadly throughout society. It is hoped that the clusters that are created will represent the levels of priority of tweet data that requires prompt attention from the government to prevent it from spreading and inciting social unrest. Based on the results of the analysis, the BiGRU fake news model yields a F1-score of 95%, while the BiGRU hate speech model yields a F1-score of 90%. Clustering data using K-Prototype in this research can reduce the number of tweet data from 13,183 to 1,791 data. These new data are considered as a priority that must be pursued in preventing social media disputes.

Time series data clusters are being researched thoroughly. The distance metric drives the development of the clustering time series. The ARIMA model is one of the models that can be employed in model-based clustering, although differing model selection criteria can lead to uncertainty in the model. In this investigation, we created a technique for ensemble distance-based time series data clustering. To express the distance between two series, five distances based on the five model selection criteria are utilized. The average of the five distances reflects the distance of two time series data. According to the simulation results, the ensemble distance method could boost clustering accuracy by more than 11%. Based on the pattern of rainfall levels, we applied our methods to find clusters of locations in the Province of West Java (Indonesia). The findings indicate that the rainfall pattern in the same cluster is similar. The cluster model is effective and feasible for representing individual models in a cluster.