The COVID-19 pandemic that occurred in early 2020 around the world has implications for Indonesia’s education sector. This pandemic led to the Indonesian government policy to study from home at all academic levels using a distance learning approach. Studies on e-learning preparedness in Indonesia involving more comprehensive samples of universities during the pandemic are still limited. This study extended samples from several public and private universities in Indonesia to get a broader picture of e-learning readiness in various faculties with diverse university online learning cultures. This study used Rasch analysis to determine the validity and reliability of the instrument and differential item functioning (DIF) analysis to identify responses based on students’ demographic profiles. The results show that most students were ready to study online, but a few were not ready. Moreover, the results show significant differences in students’ e-learning readiness based on the academic year at university, the field of study, the level of organizational e-learning culture of the university, gender, and region. This work provides an insight into student readiness to study online, especially in higher education in Indonesia. The article presents the implications of online learning practices in universities and recommendations for future e-learning research.

HIV-1 (Human immunodeficiency virus type 1) is a member of retrovirus family that could infect human and causing AIDS disease. AIDS epidemic is one of most destructive diseases in modern era. There were more than 33 million people infected by HIV until 2010. Various studies have been widely employed to design drugs that target the essential enzymes of HIV-1 that is, reverse transcriptase, protease and integrase. In this study, in silico virtual screening approach is used to find lead molecules from the library or database of natural compounds as HIV-1 reverse transcriptase inhibitor. Virtual screening against Indonesian Herbal Database using AutoDock4 performed on HIV-1 reverse transcriptase. From the virtual screening, top ten compounds were mulberrin, plucheoside A, vitexilactone, brucine N-oxide, cyanidin 3-arabinoside, alpha-mangostin, guaijaverin, erycristagallin, morusin and sanggenol N.

Cancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a meanshift filter as the data pre-processing of the images. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.

A new model for a cluster of hybrid sensors network with multi sub-clusters is proposed. The model is in particular relevant to the early warning system in a large scale monitoring system in, for example, a nuclear power plant. It mainly addresses to a safety critical system which requires real-time processes with high accuracy. The mathematical model is based on the extended conventional search algorithm with certain interactions among the nearest neighborhood of sensors. It is argued that the model could realize a highly accurate decision support system with less number of parameters. A case of one dimensional interaction function is discussed, and a simple algorithm for the model is also given.

HIV-1 (Human immunodeficiency virus type 1)׳s infection is considered as one of most harmful disease known by human, the survivability rate of the host reduced significantly when it developed into AIDS. HIV drug resistance is one of the main problems of its treatment and several drug designs have been done to find new leads compound as the cure. In this study, in silico virtual screening approach was used to find lead molecules from the library or database of natural compounds as HIV-1 protease inhibitor. Virtual screening against Indonesian Herbal Database with AutoDock was performed on HIV-1 protease. From the virtual screening, top ten compounds obtained were 8-Hydroxyapigenin 8-(2",4"-disulfatoglucuronide), Isoscutellarein 4'-methyl ether, Amaranthin, Torvanol A, Ursonic acid, 5-Carboxypyranocyanidin 3-O-(6"-O-malonyl-beta-glucopyranoside), Oleoside, Jacoumaric acid, Platanic acid and 5-Carboxypyranocyanidin 3-O-beta-glucopyranoside.