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Era revolution 4.0, universities throughout Indonesia are computer technology and the economy. This article provides sufficient detail about the course's pedagogical design and practical implementation to serve as a model for how entrepreneurship and business issues can be integrated into a software engineering program. Courses are evaluated using learning diaries and questionnaires, as well as principal lecturer learning in each of the three sample courses The aim of this course is to provide students with an introduction to lean startup methods for ideas/innovations and further product and company development. This course will teach students about the software industry, entrepreneurship, teamwork, and lean startup methodologies and This article provides sufficient detail about the course's pedagogical design and practical implementation to serve as a model for the Course to be evaluated using learning and questionnaires.

The current situation of the Covid-19 pandemic is currently increasing public concern about the community. The government has especially recommended Stay at Home and the implementation of PSBB in various regions. One of the concerns is when the election of regional leaders to the general chairman. Even though there is already a safeguard regulation, this is not considered safe in the current Covid-19 pandemic. The solution in this research is the use of a blockchain-based E-voting system to help tackle election unrest during Covid-19. Where e-voting with blockchain technology can be carried out anywhere through the device without the need to be present in the voting booth, reducing data fraud, accurate and decentralized voting results that can be accessed by the public in real-time. The use of cryptographic protocols is applied for data transfer between system components as well as valid system security. This research method uses SUS trial analysis in a significant system of the Covid-19 pandemic situation. The implication that the SUS Score analysis shows 90 shows an acceptable E-voting system, meaning that the community can accept it because it brings positive and significant impacts such as effectiveness and efficiency.

Recent advancements in the area of drug discovery using artificial intelligence made it possible to speed up the hunt for new pharmaceuticals. Drugs like arbidol, atazanavir, remdesivir & favipiravir are under testing phase to cure COVID-19. In this paper, we present systematic study of AI based drug discovery techniques suitable for COVID-19 detection.

The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis, but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison with the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity.