The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.

We propose a medical named entity recognition for medical question answering system with Indonesian language. The aim is to provide a good medical named entity grammar by only using the available language resource. Our strategy here is to build the features most often used for the recognition and classification of medical named entities. We organize them along two different axes: word-level and list features, document and corpus features. For the reason we built our own features to Indonesian medical named entities and used it as the feature of the available with SVM Software. By using 3000 sentences, the highest accuracy score achieved is about 90%. Keywords-Medical named entity, Word-level features, Document and corpus features, SVM engine.

What is Question Generation? Abstract: The hardest things in developing the question-answer system are to raise a question that comes from natural language sentences and to find the answers to some questions relevant to the query. In this paper, the strategy to be developed is how to apply a natural language processing using a technique automatically to generate questions and answers. A number of new ideas have been explored including a semantic-based template using a combination of semantic role labeling (SRL) with the predicate argument (PA) to create a semantic pattern within the scope of medical Indonesian sentences. It was more focused on Question Generating (QG) with a discourse task involving the following three steps: (1) Parsing the labeling of semantic-based element PICO with progression to PPPICCOODTQ (Problem, Patient, Intervention, Compare, Control, Outcome, Organs, Drug, Time, Quantity); (2) Identification and Transformation sentence; and (3) Filtering for answering Question construction. This study has presented a new approach by utilizing the semantic role labeling and flexibility template. This approach achieved the accuracy values of 0.80 simple sentence. The results showed the improvement of the performance of question generation from the information on medical outcomes.

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