Human-Computer Interaction (HCI) is a scientific field to determine user characteristics or so-called persona. HCI describes a system that must be easy to use, provides security to users, easy to learn and has usability. The purpose of the study was conducted to obtain various perspectives on the use of software in the InfoKHS University of Muhammadiyah Malang (UMM) academic system according to user characteristics so that the design of software requirements is expected to be representative of various types of users. The HCI assessment is carried out in software development (SD) focusing on the elicitation of needs. Focus on using user methods. As an analysis of user needs. Qualitative data were analyzed based on country hypotheses obtained for the first time in this research phase. In this study, a User Persona was done with an iterative method to ensure each phase was validated. The results show that iterations are needed several times to get the use of detailed cases diagram of each user's needs.

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Facing the news on the internet about the spreading of Corona virus disease 2019 (COVID-19) is challenging because it is required a long time to get valuable information from the news. Deep learning has a significant impact on NLP research. However, the deep learning models used in several studies, especially in document summary, still have a deficiency. For example, the maximum output of long text provides incorrectly. The other results are redundant, or the characters repeatedly appeared so that the resulting sentences were less organized, and the recall value obtained was low. This study aims to summarize using a deep learning model implemented to COVID-19 news documents. We proposed transformer as base language models with architectural modification as the basis for designing the model to improve results significantly in document summarization. We make a transformer-based architecture model with encoder and decoder that can be done several times repeatedly and make a comparison of layer modifications based on scoring. From the resulting experiment used, ROUGE-1 and ROUGE-2 show the good performance for the proposed model with scores 0.58 and 0.42, respectively, with a training time of 11438 seconds. The model proposed was evidently effective in improving result performance in abstractive document summarization.

Testing QA Article