Digital Transformation During the Pandemic Performed by SMEs in ASEAN Countries: A Review of Empirical Studies

SMEs have been the backbone of numerous economies all over the world. But since Covid-19 pandemic hit many economies, it changed practically every part and component of SMEs. In response to the pandemic, one transition has been accelerated, namely digital transformation. Although previous studies on digital transformation performed by SMEs in ASEAN countries have been conducted, currently, there is no single empirical study that compare digital transformation conducted by SMEs across ASEAN countries. Therefore, we discuss and reflect on the implementation of digital transformation by SMEs in ASEAN countries by reviewing existing empirical studies on SMEs' digital transformation published between 2020–2022 are reviewed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Design and Development Integrated Sensor System for Measuring Body Temperature and Heart Rate

Heart rate and body temperature are some of the important components of a person's main vital signs that need to be monitored regularly and periodically. The detection system technology continues to develop which allows a person to detect his own condition, to avoid exposure to COVID-19. However, the tools that are developing in the market are quite expensive and sometimes complex in operation because they are external products, so that it becomes a difficulty in itself. For this reason, it is important to design a detection device with sensor components that exist in the country and with a simple design so that it is easy to operate and inexpensive. In this paper, utilizing pulse sensors and AD8232 sensors to detect heart rate and MLX90614 sensors to measure body temperature, then NodeMCU ESP8266 to process sensor signals received and will be forwarded to the Display (LCD) to display the results carry out the design and development of an integrated sensor system. From the research results, the accuracy of the MLX90614 temperature sensor is very good with the achievement of 99.24% and the pulse sensor with the achievement of 98.86%. For the test results on each sample obtained accuracy values of 98.4% and 99% for the temperature sensor, and 92.3% and 92.2% for the pulse sensor, respectively. From these results, it is very clear that the sensor design deserves to be promoted as a quality product. © 2022 IEEE.

Analysis of The Application of Electronic Scientific Discussion Group on Student Learning Outcomes

The COVID-19 pandemic has had an impact on every line of human life, including education. This study aims to analyze the application of online learning based on Electronic Scientific Discussion Group (ESDG) in the Biology Tadris Study Program, Faculty of Tarbiyah and Teacher Training, State Islamic University of North Sumatra. This study uses quantitative and qualitative research designs with research procedures including: collecting data, uniting data, reducing data with analytical techniques, and concluding the data findings. The research instrument used was in the form of observation sheets and interviews. The data were collected by interview and observation techniques, then the data were analyzed using inferential statistical techniques and the Miles and Huberman approach. The results of the study indicate that the application of ESDG is carried out in three learning series that are integrated in virtual learning. By applying ESDG student learning outcomes have increased with the highest increase in learning outcomes in the four observation classes of: 86.9%, 84.8%, 87.4%, and 88.2%. The results of the study can be concluded that the application of ESDG has a significant effect on improving learning outcomes in online learning. © 2022 American Institute of Physics Inc.. All rights reserved.

The COVID-19 Pandemic Impact on Corporate Dividend Policy of Sustainable and Responsible Investment in Indonesia: Static and Dynamic Panel Data Model Comparison

This research investigates the impact of crisis due to the COVID-19 pandemic on the dividend policy of green index companies in Indonesia, namely the Sustainable and Responsible Investment (SRI) by Biodiversity (KEHATI) Foundation, or SRI-KEHATI indexed companies. The purposive sampling technique was used to collect data from companies listed from 2014 to 2020, using static and dynamic panel data models. From the several panel data models tested, the static panel data regression with random effects model (REM) and fixed effect model (FEM) uses the least square dummy variable-robust standard error (LSDV-RSE) technique are the best econometric models feasible. The system generalized method of moments (SYS-GMM) is used as a suitable econometric model with a robustness test used to determine static panel data regression. It is reported that SRI-KEHATI indexed companies tend to distribute dividends positively during this crisis, and is also statistically proven robust. This gives a positive signal to the capital market concerning the sluggish trading activity. The market reaction test, using two-approaches, showed that this business did not provide a positive reaction to the capital market, which turned out to be pessimistic. Furthermore, profitability and financial leverage have a robust effect, while dividends from the previous year affect dividend policy on the static panel data model, and firm size affect dividend policy on SYS-GMM. Predictors that proved influential with a direction not in line with the hypothesis were investment opportunities on REM and SYS-GMM, and firm age on SYS-GMM. The parameter estimation that passes the model specification test is feasible, whiles the biased and inconsistency of parameter estimation due to the alleged correlation between ui , t and PYDi,t failed to occur in static panel data regression. The endogeneity issue was resolved by dynamic panel data regression with the strongest corrective effect. This research can be used as a reference for investors to obtain optimal returns on green index companies in the country. An optimal dividend policy can increase the value of the SRI-KEHATI indexed companies;therefore, it can contribute optimally to sustainability and responsibility for social and environmental aspects. © 2022 by the authors.

Forecasting Vaccination Growth for COVID-19 using Machine Learning

In this digital era, machine learning (ML) is becoming more common in the healthcare industry. It plays many essential roles in the medical field including clinical forecasting, visualization, and even automated diagnostics. This paper focuses on the future prediction of COVID-19 vaccination rates in different countries. Considering how destructive the novel Coronavirus has been and its continuous mutation and spread, clinical interventions such as vaccines serve as a ray of hope for many individuals. As of 2021, an estimated total of 8,687,201,202 vaccine doses by numerous biopharmaceutical manufacturers have been administered worldwide [1]. This study intends to estimate the probable increase or decrease in global vaccination rates, as well as analyze the correlation between future trends of daily vaccinations and new COVID-19 cases, along with deaths and reproduction rates. Three models were utilized in forecasting and comparing the overall prediction toward the COVID19 vaccine rates;Auto-Regressive Integrated Moving Average (ARIMA), an ML approach, Long-Short Term Memory (LSTM), an artificial Recurrent Neural Networks (RNN), and Prophet which is based on an additive model. The Vector Autoregression (VAR) model will also be utilized to compare COVID-19 cases, deaths and reproduction rates to that of COVID-19 vaccine growth. ARIMA resulted to be the best model, while Prophet turned out to be the worst-performing model. In general, our comparison of employing the ARIMA model vs the other three results in the conclusion that adopting this method shows to be a more effective approach in projecting vaccination growth in the future. Furthermore, a visible increase in future daily vaccinations can be seen to be correlated with the increase in COVID-19 cases, deaths reproduction rates, and a fluctuating trend in COVID-19 deaths. © 2022 IEEE.

ASEAN-REPUBLIC OF KOREA DIGITAL PARTNERSHIP:THE IMPERATIVES OF TRANS-REGIONAL COOPERATION IN CONTROLLING COVID-19

As the raging COVID-19 pandemic continues to spread and bring the world to its knees, governments around the world are still struggling to find effective solutions to prevent the further spread of the disease and its tremendous impact on human life. However, the Republic of South Korea (ROK) has successfully responded to this problem with its advanced technology. Conversely, ASEAN countries are still grappling to manage enormous COVID-19 impacts due to the lack of equal technological access. Hence, this study was aimed at exploring and outlining the possible adoptions of ASEAN-Korea Digital Partnership in addressing contemporary and post COVID-19 challenges. As an effort to meet the purpose of this study, the pragmatism of a qualitative methodology was deemed suitable to assess and compare the current conditions and challenges faced by ASEAN and the ROK. After being amalgamated in extensive and strategic relations for more than 30 years, ASEAN and ROK have accomplished outstanding achievements and a blossoming joint development, specifically in technological cooperation. Thus, the ASEAN-Korea Digital Partnership is more than feasible in its goal of supporting the technological advancements of ASEAN and open an ideal market for the ROK's technological production. Considering the economic, technological, and political challenges and incompatibilities, it can be reasonably assumed that the possible digital partnerships are 5G interconnectivity, surveillance system, e-commerce, and cybersecurity cooperation. It is hoped that the present study can serve as a new alternative perspective for a post COVID-19 ASEAN-Korea Digital Partnership trans-regional cooperation.

Evaluating Deep Learning for CT Scan COVID-19 Automatic Detection

Aside from Reverse Transcription Polymerase Chain Reaction (RT-PCR), another common method to check for the 2019 novel Coronavirus disease (COVID-19) is by using a chest CT scan. Imaging data is profoundly useful in the diagnosis, detection of complications, and prognostication of COVID-19, displaying various spots in the lungs affected by the viral infection. The complex results often require some time before radiologists can analyze them and are more prone to human errors. Inventions of medical assisting tools, through enhancement of artificial intelligence, are crucial in fighting the COVID-19 pandemic through automation of classifications and the future of medicine. To overcome the above challenges, this paper aims to propose and evaluate the performance between Convolution Neural Network (CNN) and Transfer Learning (TL) in the detection of COVID-19 infections from a Lung CT Scan. Gradient-Weighted Class Activation Mapping (Grad-CAM) will also be utilized to display the infected areas in the lungs for explorative experiments. Transfer-learning using our pre-trained model resulted in a detection accuracy result of 89% while our proposed CNN demonstrated the best result in terms of classification accuracy at 97%. Training time required for the two frameworks are 12 and 22 minutes respectively. By and large, our comparison of using the CNN model versus the pre-trained model gives rise to the conclusion that using the former method proves to be a more effective technique of COVID-19 detection by CT-scan. © 2021 IEEE.

COVID-19 Pandemic and Adaptive Shopping Patterns: An Insight from Indonesian Consumers

Although existing studies on consumers typology are extensively conducted, insights on consumers typology in adapting their shopping attitude and behaviour during the COVID-19 pandemic remain unexplored. Current studies on consumer responses to the COVID-19 pandemic tend to focus on the following themes: panic buying behaviour, consumer spending and consumer consumption. This study explores a typology of adaptive shopping patterns in response to the COVID-19 pandemic. The study involved a survey of 465 Indonesian consumers. Principal component analysis is used to identify the variables related to adaptive shopping patterns. Cluster analysis of the factor scores obtained on the adaptive shopping attitude and behaviour revealed the typology of Indonesian shoppers’ adaptive patterns. Multivariate Analysis of Variance (MANOVA) analysis is used to profile the identified clusters based on attitude, behaviour and demographic characteristics. Results revealed five adaptive shopping patterns with substantial differences among them. This study provides in-depth information about the profile of Indonesian shoppers’ adaptive patterns that would help retailers in understanding consumers and choosing their target group. The major contribution of this study is providing segmentation on shopping adaptive patterns in the context of the COVID-19 pandemic which presents interesting differences compared with previous studies. This study reveals new insights on shoppers’ adaptive attitude and behaviour as consumers coped with the pandemic. © 2021 International Management Institute, New Delhi.

Predictors of COVID-19 severity: a systematic review and meta-analysis

Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched and extracted as of April 5, 2020. Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin;elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin;and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.