Millions of people in Indonesia traditionally return to their home town every year in a tradition called "mudik". Unlike previous years, the annual tradition has become a cause for concern after COVID-19. This article presents the potential of mudik flows patterns during the COVID-19 pandemic, which were divided into 11 regions. We use a circlize plot chord diagram to show the potential of mudik flows patterns based on primary survey data on community perceptions regarding mobility and transportation during COVID-19. The results show that the most massive mudik flow is expected to occur from Jabodetabek to Central Java. Jabodetabek is the highest mudik origin, while Central Java and East Java are the highest mudik destinations. We suggest that government should anticipate the spread of COVID-19 by limiting mobility as they have been done this year. In addition, this must also be supported by citizen's awareness and coordination between local governments.
Studi Kependudukan menjadi penting karena data semakin banyak digunakan untuk berbagai rencana pembangunan, dan memahami berbagai fenomena di masyarakat modern. Data sensus dan survei menjadi sumber utama kajian data kependudukan dalam Studi Kependudukan. Namun dalam kondisi darurat seperti terjadi perang atau konflik, bencana alam dan wabah penyakit maka pendataan secara tatap muka mungkin tidak dapat dilaksanakan secara tepat dan menyeluruh. Dalam kondisi ini sumber data lain yang dapat dimanfaatkan adalah dengan simulasi mikro menggunakan Agent Based Modelling (ABM). ABM adalah metode komputasional yang memungkinkan peneliti menciptakan, menganalisa, melakukan eksperimen dari suatu model yang terdiri dari sejumlah agen yang saling berinteraksi dengan lingkungan. Sehingga melalui simulasi mikro dengan ABM dapat diperoleh data populasi mikro, penjelasan fenomena sosial, prediksi dan estimasi kebutuhan pada suatu bidang. Berdasarkan kajian literatur diketahui ABM memiliki potensi besar untuk digunakan dalam penelitian Studi Kependudukan. Untuk itu paper ini bertujuan memperkenalkan dan menjelaskan tentang ABM, mengkaji potensi dan tantangan aplikasi ABM di bidang Studi Kependudukan. Sebuah ilustrasi aplikasi ABM tentang model penyebaran wabah COVID-19 dengan skema Susceptible-Exposed-Infectious-Removed (SEIR) juga diberikan dalam paper ini untuk menunjukkan potensi ABM dalam perencanaan kebijakan kependudukan bidang kesehatan.
The increasing number of disasters in Indonesia exerts significant economic loss and human casualties, whereas Indonesia has focused on disaster risk reduction. Recent studies are lacking to explore how economic growth influences the impact of disasters. This paper aims to seek a deeper understanding of how welfare that represented by the human development index influences the impact of disaster on the people. We implement the concept of welfare and risk reduction as an approach to analyze disaster and welfare data with a focus on flood events in 2008-2018. We obtained the data from BNPB and Statistics Indonesia. We fitted a multivariable negative binomial regression with ‘human losses from deaths’ as the outcome and provincial Human Development Index (HDI) and provincial gross domestic product (GDP). The results suggest that Provincial HDI is negatively significant associated with human losses from deaths. However, GDP was also found to be positively significant associated, albeit less strongly, with human losses from deaths. These associations bring about potentially significant policy implications.
The rising risks of climate change and Indonesia's dynamic urban and industrial development has meant that many areas have become vulnerable to flood. Historical data from 1811 to 2017 clearly shows how floods have causing major disasters across Indonesia's archipelago, and data from 1990 indicate that the number of deaths due to floods and heavy rains has risen far faster than any other hydroclimatic disasters in the same period. As the intense urban and floodplain development in Indonesia shows no sign of slowing down, it is possible the Indonesia could expect an increase in the number of people being exposed to flood risks. Therefore, the trade-offs between flood protection and the relocation of economic activities to safer areas are likely to remain a major public debate (Strauss, Kulp, & Levermann, 2015). However, when urban areas repeatedly suffer from floods, why don't the people and businesses move to safer areas or even leave the city, and why do they tend to restore these vulnerable locations? This paper seeks to understand the public's perceptions regarding the social construction of risk and the degree to which these perceptions are harnessed to develop a sustainable resilience. This paper explores the public perceptions of 926 urban residents in Indonesia, the data for which were extracted from the 4,985-person nationwide Climate Asia survey in Indonesia. This study aims to contribute to future urban development, population studies, and disaster risk. To urban Indonesian, religious and moral beliefs was the most important value. This value lead to people's higher susceptibility towards risk. In daily basis, risk perception translated to the higher value of worries on not having clean water, the urgency of having enough access to health care and adequate food for the family. Current flood management tends to be focused more on structural measures, very little attention is paid to the social processes involved in building a resilient society This study emphasis on the fact that to build sustainable resilience, it is essential to understand the public's perception of the social construction of risk.
COVID-19 pandemic has impacted the labor market in Indonesia, such as decreasing wages and layoff. The decline of worker demand may drive job turnover into the informal sector. Using Google Trends data, this study aims to explore the existence of informal job turnover signals during physical restriction and new normal in Indonesia. Four keyword categories: “PHK”, “situs loker”, “kurir”, “driver online”, and “berjualan” were used to analyze layoff and search the informal job opportunity. The trend analysis results using local regression (LOESS) and difference-in-differences (DD) methods found a signal of informal job turnover during physical restriction and new normal. The job turnover signal was shown by increasing search intensity about “PHK” (layoff), followed by the search intensity of job opportunities such “berjualan online” (online selling) and “lowongan kurir” (courier jobs).
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