ABSTRACT
Telecommunication technology continues to develop starting from 1G, 2G, 3G, 4G, and currently entering the 5G era. The Global System for Mobile Communications (GSM) based telecommunication industry in Indonesia consists of three big names: Telkomsel, XL, and Indosat. During the Covid-19 pandemic, activities carried out outside the home should be done online. People hope that the internet network can work properly. However, the reality is not as expected, because many networks are experiencing slow internet problems and many complaints are delivered through social media. Therefore, this research aims to find the insight opinions that have been conveyed to the telecommunications operator in social media. This research used the Convolutional Neural Network (CNN) algorithm to classify text sentiment (negative or positive) about telecommunication providers. The experiment with text data from Twitter is conducted after preprocessing and weighting of the Word2Vec process. The confusion matrix experiment shows that the CNN algorithm's performance reaches an average accuracy value of around 86.22%. The experiment was carried out by dividing the training data and testing the data 5 times in 10 times. The study results indicated that disruption of cellular telecommunications operators provided many sentiments, especially negative sentiment at the beginning of the COVID-19 pandemic. © 2022 IEEE.
ABSTRACT
During the COVID-19 pandemic, various activities of people outside the home were disrupted and made people move more indoors. For some companies take advantage of this pandemic period as their advantage, especially digital game industry companies. Various games have been released and promoted, these games are published on various game platforms. Currently, Steam is one of the biggest gaming platforms. On this platform, there are a lot of games offered by game developers and provide game pages that are currently popular. However, the website does not provide the popularity level of the currently popular games. This causes ambiguity in determining which games have high, medium, or low popularity. This study tries to create a machine learning model to cluster these games into groups using Agglomerative Hierarchical Clusterin. The distance measure used is euclidean, cosine and manhattan/cityblock and uses single, average, complete and ward linkage. Based on the evaluation results, the best cluster results are the silhouette value of 0.639 and the calinski-harabasz value of 90.192. © 2022 IEEE.
ABSTRACT
Health is an essential thing in carrying out human activities. The COVID-19 pandemic has made people aware of the importance of maintaining health and hygiene for individuals, families, and the surrounding community. All countries, including Indonesia, are impacted by the COVID-19 pandemic, which has undoubtedly changed health behavior in the community. This study aims to reveal changes in health behavior during the pandemic through conversations on social media such as Twitter. The study was conducted using the Latent Dirichlet Allocation (LDA) method to analyze changes in Indonesian citizens' health behavior during the pandemic through social media analysis. The results of social media analysis using LDA on 495,740 tweet data indicate that it is true that there has been a change in public health behavior. At the beginning of the pandemic, many people still did not believe that various hoaxes were spread, and it was difficult to comply with health protocols. Hence, the government massively appealed to make regulations to break the chain of the spread of COVID-19. However, at a critical time with many victims falling, the public became more aware, maintained health protocols, followed the vaccination program, and finally, people got used to coexistence with COVID-19. These results indicate that the Indonesian people are wiser in dealing with the COVID-19 pandemic and following the applicable health protocols. © 2022 IEEE.
ABSTRACT
Coronavirus Disease 2019 (COVID-19) is a new disease discovered in 2019 in Wuhan, China, and then spread worldwide. Many victims have confirmed varying positive levels of infection based on the patient's immunity. This study aimed to predict the chances of COVID-19 patients' recovery based on the patient's symptoms and conditions. The method used is the K-Nearest Neighbor (KNN) algorithm. KNN produces two classes of predictions: the chance of recovering or the possibility of dying. Based on the experimental results on 496 data from patients who were confirmed positive for COVID-19, KNN predicted the chances of recovery for patients with confirmed COVID-19 with an average accuracy of 88.16%. A prediction system for the chance of recovery for COVID-19 patients is constructed by choosing the best model from five test scenarios based on the given k value. The best model is at a value of k equal to 4, with an accuracy value of 88.8%. © 2022 IEEE.
ABSTRACT
During the COVID-19 pandemic, there has been an increase in communication through social media. However, this is not accompanied by the level of citizen discipline where the high spread of false issues causes problems in the social and political fields. This study aims to look at the views of the community during the COVID-19 pandemic regarding Social and Political. The research method uses Latent Dirichlet Allocation Analysis to extract topic cluster of political and social during COVID-19 pandemic in Indonesia. The results of this study indicate a public view that considers the COVID-19 pandemic to be a tool, interest, or political game, and social concerns have increased considerably during the COVID-19 pandemic. During COVID-19, the increase in public communication through social media has become a driving force for other social activities that can ease the burden on the community. Besides that, the community seems wise enough to choose which news is fake and what is true. © 2022 IEEE.