Publikasi Ilmiah

The aims of this study are to identify risk factors and develop a composite risk factor of initial stage of COVID-19 pandemic in regency level in Indonesia. Three risk factors, i.e., exposure, transmission and susceptibility, are investigated. Multivariate regression, and Canonical correlation analysis are implemented to measure the association between the risk factors and the initial stage of reported COVID -19 cases. The result reveals strong correlation between the composite risk factor and the number of COVID-19 cases at the initial stage of pandemic. The influence of population density, percentage of people commuting, international exposures, and number of public places which prone to COVID-19 transmission are observed. Large regencies and cities, mostly in Java, have high risk score. The largest risk score owned by regencies that are part of the Jakarta Metropolitan Area. 

There are two premises regarding the relationship between mobility and COVID19, namely: (1) mobility affects the spread of COVID-19, or (2) the spread of COVID-19 affects mobility. This paper further explores both premises to analyze community responses to COVID-19 pandemic using Google Mobility Index (mobility) and the COVID-19 Spread Risk Index (risk) of Indonesia. Crosscorrelogram of both indices is examined to determine optimum values called Risk Detection Time (Rdt). A scatter plot of Rdt and its correlation coefficient resulted Mobility-COVID-19 Impact Quadrant which maps the community responses into four zones based on quadrant ‘conscious–competence’ framework. The results confirmed both premises: (1) risk can be triggered by mobility in the previous few days, or (2) mobility can represent the community responses to risk information in the previous few days. Regarding the mobility restriction implemented in Indonesia, the analysis shows that the community responses leaped from Learning zone in PSBB period (15/03/2020 to 31/05/2020) to the Recovery zone in the New Normal and PPKM period (01/06/2020 to 02/07/2021). However, the policy was late responded so that the recovery target did not go as expected and brought the community into Fear and Uncertainty zone in the Emergency PPKM period (starting from 03/07/2021). 

All countries affected by the COVID-19 pandemic have established several policies to control the spread of the disease. The government of Indonesia has enforced a work-from-home policy and large-scale social restrictions in most regions that result in the changes in community mobility in various categories of places. This study aims to (1) investigate the impact of large-scale restrictions on provinciallevel mobility in Indonesia, (2) categorize provinces based on mobility patterns, and (3) investigate regional socio-economic characteristics that may lead to different mobility patterns. This study utilized Provincial-level Google Mobility Index, Flight data scraped from daily web, and regional characteristics (e.g., poverty rate, percentages of informal workers). A Dynamic Time Warping method was employed to investigate the clusters of mobility. The study shows an intense trade-off of mobility pattern between residential areas and public areas. In general, during the first 2.5 months of the pandemic, people had reduced their activities in public areas and preferred to stay at home. Meanwhile, provinces have different mobility patterns from each other during the period of the large-scale restrictions. The differences in mobility are mainly led by the percentage of formal workers in each region. 

The COVID-19 outbreak is not only talking about health crises but also social and economic crises all over the world. In Indonesia, the outbreak has shaken almost all business sectors, however it seems to bring a silver lining for e-commerce sectors since the pandemic has developed online shopping habits. During the pandemic, the impact of COVID-19 on the Indonesian economy needs to be updated from time to time to be used on quick policymaking. Therefore, Big Data plays an important role to provide the information relatively fast. This paper aims to describe how Big Data i.e., marketplace data, could be used to figure the impact of COVID-19 outbreak on micro and small retailers in Indonesia. The dataset was collected regularly from a marketplace website in Indonesia from January to June 2020. To see the changing of sales during the COVID-19 period, the sales before and after social distancing policy implementation are compared. The result showed that the online marketplace in Indonesia is dominated by micro retailers based on the number of products sold in the marketplace. The total revenue of micro retailers gives a significant increase during the pandemic. Whereas for medium retailers, the increase in total revenue is seen to be lower than micro retailers’ total revenue. It indicates a positive sign for the growth of micro retailers in the online marketplace.

Data and information on the income of the large and medium trade and the manufacturing sector are essential for the government to make policies. Currently, official statistics containing this information are still carried out conventionally. There has to be a new data source that can be used as a faster and more granular alternative reference. The goal of this research is to investigate a novel technique to generate data on vehicle sales in Indonesia from Big Data that can support and provide an overview of the manufacturing sector in real-time and may be used as a comparative or complementary to official statistics. This research uses a web scraping method from one of the largest vehicle advertiser sites in Indonesia. Vehicle sales data is collected weekly for the four vehicle types advertised in Indonesia using the HTML structure of the site. The Python programming language's Scrapy module is implemented. Data collection is carried out every week, and 358,451 vehicle advertisements have been collected from January 2019 to June 2021. The findings suggest that vehicle sales data from Big Data can be used as a comparative or complementary to official statistics, as well as supporting data in the manufacturing sector. By using web scraping techniques, indicators that usually require more time and cost can be done in real-time at a lower budget. This new approach is expected to improve the quality of official statistics in the manufacturing sector.