The impact of large-scale social restriction and odd-even policies on transportation mobility and COVID-19 positivity rates in Jakarta

SARS-Cov-2 spreads quickly and continues to do so to this day. One way to limit the spread is by limiting people's mobility through transportation. The Provincial Government of Jakarta has implemented the Large-Scale Social Restriction Transitional Phase-1 since June 5th, 2020, to limit people mobility and the odd-even policies August 3rd, 2020, to limit private vehicles. To assess the effectiveness of these policies against the positivity rate of COVID-19 cases in Jakarta, we used data exploration and significant tests (pre-and post-condition). The result shows that the odd-even policy significantly impacts private transportation mobility, mobility in public transportation, and the COVID-19 positivity rate in Jakarta. The relationship between those three parameters is linearly significant. The odd-even policy stimulates people to switch from private to public transportations and increases the positivity rate of COVID-19. The odd-even policy effectively reduces the mobility of private transportation but insignificantly reduces the positivity rate of COVID-19 in Jakarta. The results can be used as insights for policy decision-makers to manage the COVID-19 pandemic. © 2023 Author(s).

Lessons Learned from Delta and Omicron Variants Transmissions Leveraging Clustering Approach by Sub-Districts in Jakarta

Various variants of COVID-19 have entered Indonesia, such as the delta and the omicron variants. The delta variant has a higher severity than the omicron variant, but the transmission rate for the omicron variant is much faster. The government encourages citizens to get booster vaccines to reduce the effect of the delta and omicron variants. The booster vaccine produced a better effect on citizens than on people who received only the two doses. Therefore, in this study, we observe the transmission of COVID-19 and the vaccine locations on the sub-districts level using the clustering approach. The data we use are COVID-19 positive cases, died, treated, and self-isolated cases. Meanwhile, the vaccination data are 1st dose, 2nd doses, stage 3 of 1st dose, and stage 3 of 2nd doses. The Dunn Index and Hubert Index methods determined the best number of clusters before the clustering process. Silhouette and Davies Bouldin are used to find better clustering between Fuzzy C-Means, K-Means, and Partition Around Medoids (PAM). The results obtained from this study showed that the K-Means method was the best with the best number of clusters, namely 3. Jagakarsa and Kebon Jeruk entered high levels at the time of the delta variant due to the COVID-19 case and vaccination spread. However, Jagakarsa and Kebon Jeruk dropped to the intermediate level during the omicron variant. The benefit of this study is to help the government pay more attention to high COVID-19 cases and low vaccine distribution. © 2022 IEEE.

Data Analysis and Synthesis of COVID-19 Patients using Deep Generative Models: A Case Study of Jakarta, Indonesia

Two years have passed since COVID-19 broke out in Indonesia. In Indonesia, the central and regional governments have used vast amounts of data on COVID-19 patients for policymaking. However, it is clear that privacy problems can arise when people use their data. Thus, it is crucial to keep COVID-19 data private, using synthetic data publishing (SDP). One of the well-known SDP methods is by using deep generative models. This study explores the usage of deep generative models to synthesise COVID-19 individual data. The deep generative models used in this paper are Generative Adversarial Networks (GAN), Adversarial Autoencoders (AAE), and Adversarial Variational Bayes (AVB). This study found that AAE and AVB outperform GAN in loss, distribution, and privacy preservation, mainly when using the Wasserstein approach. Furthermore, the synthetic data produced predictions in the real dataset with sensitivity and an F1 score of more than 0.8. Unfortunately, the synthetic data produced still has drawbacks and biases, especially in conducting statistical models. Therefore, it is essential to improve the deep generative models, especially in maintaining the statistical guarantee of the dataset. © 2022 IEEE.

COVID-19 Mortality Risk Factors Using Survival Analysis: A Case Study of Jakarta, Indonesia

At the end of 2021 Q2, coronavirus disease 2019 (COVID-19) in Indonesia experienced a continuous increase in positivity and mortality rates. There are limited studies regarding the factors of COVID-19 mortality in Indonesia with a more balanced dataset. The previous studies only implemented logistic regression, sensitive to the imbalanced dataset. Meanwhile, other countries implemented survival analysis to overcome the problem. Most survival analyses using Cox proportional hazard (CPH) model require the variables to be time-independent. To this end, this study aims to identify the risk factors for COVID-19 mortality in Indonesia using a survival analysis approach using Jakarta as a case study. We use the Piecewise Exponential Model (PEM) to overcome the time-dependent problem in CPH. The findings show that the COVID-19 mortality does not differ the gender. In contrast, it differs the elderly with 3.5 times higher to be deceased. Dyspnea, malaise, and pneumonia are the primary symptoms associated with COVID-19 mortality. From the comorbidities, diabetes and chronic disease are related to COVID-19, while hypertension and heart attack are still considerable in clustered contexts. The advanced treatment using intubation and extra corporeal membrane oxygenation (ECMO) produces a relatively large hazard risk of COVID-19 mortality. Based on the findings, we suggest that collaboration among the government, society, and hospitals is vital in overcoming the COVID-19 pandemic and minimizing the COVID-19 death.

Sentiment Classification Against the Public Activity Restrictions Policy in Jakarta Using Machine Learning Models

To stop the spread of the COVID-19, the Indonesian government implemented community activities restrictions enforcement (in Indonesian language: Pemberlakuan Pembatasan Kegiatan Masyarakat or PPKM) starting from January 2021. The term PPKM applied are PPKM Mikro (in Indonesian language) or Micro PPKM, PPKM Darurat (in Indonesian language) or Emergency PPKM, and PPKM Level 1-4 or Level 1-4 PPKM. On the other hand, the existing research mostly used Twitter as the data source to do sentiment classification. Therefore, we aimed to classify social media comments on Facebook and YouTube on Level 1-4 PPKM policy in Jakarta. We used "PPKM Jakarta"as the keyword topic in August - September 2021 when Level 1-4 PPKM was ongoing. In addition, we compared datasets composition, machine learning models, and features extraction. Random Forest, Naive Bayes, and Logistic Regression were performed as the machine learning models due to they were the top three models on the previous research. We extracted word unigram, word bigram, character trigram, and character quadrigram as the feature extraction. The highest average F-measure was obtained with a 79.6% score of the Logistic Regression model using character quadrigram extraction. We found that comments from Facebook and YouTube were dominated by neutral sentiment (49.8%) with this setup. It means the people of Jakarta started to trust the government in handling the COVID-19 pandemic. Through word cloud analysis, it is recommended that social assistance be reviewed for those directly affected. © 2021 IEEE.

The Impact of Large-Scale Social Restriction and Odd-Even Policies during COVID-19 Pandemic to Traffic Congestion and Air Pollution in Jakarta

The COVID-19 pandemic has had a global impact on transportation mobility and air pollution, including Jakarta as the capital and busiest city in Indonesia. This paper reports the impact of two policies imposed by the Governor of Jakarta, namely the odd-even and the large-scale social restriction (PSBB) transitional phase-1, against the traffic congestion and air pollution quality in Jakarta during the COVID-19 pandemic. This paper investigates the odd-even and PSBB policy impact using paired T-Test. Moreover, this study assesses the relationship between traffic congestion and air pollution using the Pearson correlation. The result shows that the odd-even policy does affect significant only on MH Thamrin Street. Furthermore, the odd-even policy does not significantly affect air pollution reduction in Jakarta. This study also found that there is no meaningful relationship between traffic congestion and air pollution. These results can be used to reference future data-driven policy improvement on traffic congestion and air pollution management in Jakarta and other cities. © 2021 IEEE.

An evidence-based culture: COVID-19 positivity factors during the asymptomatic occurrence in Jakarta, lndonesia

Coronavirus disease 2019 (COVID-19) has been a global disaster, with over 746,312 confirmed cases and still counting in Indonesia, especially Jakarta, which has about 50 per cent asymptomatic confirmed cases. This paper aims to investigate the persistent factors of COVID-19 diagnosis using four scenarios of asymptomatic inclusion. We use Bayesian Logistic Regression to identify the factors of COVID-19 positivity, which can address issues in the traditional approach such as overfitting and uncertainty. This study discovers three main findings: (1) COVID-19 can infect people regardless of age;(2) Among twelve symptoms of coronavirus (COVID-19), five symptoms increase the COVID-19 likelihood, and two symptoms decrease the possibility of COVID-19 infection;and (3) From an epidemiological perspective, the contact history rises the probability of COVID-19, while healthcare workers and people who did travel are less likely to become infected from COVID-19. Therefore given this study, it is essential to be attentive to the people who have the symptoms and contact history. Surprisingly, health care workers and travelers who apply health protocols strictly according to the rules have a low risk of COVID19 infection. © 2021 The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Video Game Addiction Among Students During COVID-19 Pandemic Based on Regulatory Focus Theory and Interpersonal Competence

Video game addiction is one of the mental health problems due to the uncontrolled activities in accessing video game platforms. This study aimed to identify the tendencies of video game addiction among Senior High School students based on the aspects of Regulatory Focus Theory and interpersonal competence. It implemented a quantitative descriptive model with a 2 x 2 factorial design. A total of 1046 students participated in the survey. The findings revealed the increasing video game addiction cases among the students during the COVID-19 pandemic. The students with a high promotion focus and a high interpersonal competence as well as those with a low prevention focus and a low interpersonal competence tended to experience video game addiction.

Forecasting of COVID-19 Cases in Jakarta using Poisson Autoregression

COVID-19 is currently become a global problem, including in Jakarta, Indonesia. There have been many approaches to predict COVID-19 occurrence, including the forecasting approach. However, the traditional forecasting method, particularly machine learning, often does not consider the condition of the data, although it has forms of the count, such as the number of cases. This study employs an autoregression model using Poisson distribution in predicting the COVID-19 future cases, namely the positive and recovery number. We compare the Poisson Autoregression with several well-known forecasting methods, namely ARIMA, Exponential Smoothing, BATS, and Prophet. This study found that Poisson Autoregression could create an accurate prediction with MAPE below 20% and tend to follows the actual data for the next 8 to 14 days to the future. Thus, this approach can forecast the future cases of COVID-19 and other cases that use count data in Jakarta, like the number of citizen complaints or transportation context. © 2021 IEEE.

Contributing Clinical Attributes to COVID-19 Mortality in Jakarta: Machine Learning Study

Since December 2019, we have lived in a pandemic era of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Medical records of COVID-19 patients have been reported and analyzed worldwide. The Health Agency of Jakarta, Indonesia, collected clinical symptoms, demographics, travel history, and mortality information from March 2020 up to now. Despite massive research on COVID-19 patients' data, the significant clinical symptoms that lead to COVID-19 mortality in Jakarta have not been well described to the best of the authors' knowledge. We extracted the COVID-19 records in Jakarta and compared them between patients who were discharged and deceased. This paper examines each clinical symptom's importance to mortality using machine learning techniques, namely weighted Artificial Neural Network, Decision Tree, and Random Forest. We observed that Pneumonia, Shortness of Breath, Malaise, Hypertension, Fever, and Runny Nose are the top six significant clinical symptoms that lead to deaths in Jakarta. We suggest medical experts become more cautious with these symptoms. Also, in medical facilities, these symptoms can be used as prescreening before entering the facilities. © 2021 IEEE.

Predicting video game addiction: The effects of composite regulatory focus and interpersonal competence among indonesian teenagers during covid-19 pandemic

Video game addiction is recognized as a mental health problem caused by uncontrolled access to video gaming platforms. Proper assistance and counseling programs based on the addiction causing factors are required to reduce the tendencies of video game addiction. The study aims to identify the correlation between regulatory focus theory and interpersonal competence towards the tendencies of video game addiction. The study is a type of cross-sectional research with the adapted psychological scales. A total of 136 teenagers, consisting of 86 males and 50 females participated in the survey of self-reported video game addiction. The data were analyzed using multiple regression analysis. The findings revealed that regulatory focus and interpersonal competence simultaneously had a significant effect on the tendencies of video game addiction behavior. The findings of the study can provide the basis to provide proper assistance services, in an attempt to reduce the tendencies of video game addiction among teenagers. © Nugraha, Y., Awalya, A., & Mulawarman, M. (2021).

A computer vision-based object detection and counting for COVID-19 protocol compliance: A case study of Jakarta

The chaotic world situation caused by the SARS-CoV- 2 virus (COVID-19 pandemic) has hampered many sectors of human activity, especially in activities that require physical interactions. Thus, requiring social restrictions for those sectors that are affected. This paper reports the analysis of the proposed system for monitoring and supporting public activities in order to carry out social restrictions, specifically in the DKI Jakarta province. The proposed systems are YOLO and MobileNet SSD as its main weight to help this detection system with 30% and 40% confidence, respectively. The results of object counting and physical distancing are expected to be a guideline for public complaints in the future by using several CCTV locations points with better image quality and better angles. © 2020 IEEE.

A study of coping stress strategies on psychological well-being during the COVID-19 pandemic in Jabodetabek Area

Large-Scale Social Limitations-related policies enacted by the Provincial Government of the Special Capital Region of Jakarta evoked an adaptation process to changes in their usual life patterns. Such adaptation processes are suspected to create new problems, which might become stressors. This research aims to perceive the effect of coping strategies on the psychological being of Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi) citizens during the Large- Scale Social Limitations period. Results showed that there was a significant relationship between coping strategies and psychological well-being (p=0.000<0.005). Research also found that most respondents cope by employing the emotion-focused coping system. It was strongly suspected that citizens were able to cope and manage stressors during the pandemic by doing self-improvement activities and trying to connect with their social network (friendship or work-related) with existing technological platforms. On the other hand, it was also suspected that the high number of respondents with emotion-focused coping was a result of feelings of helplessness in controlling problems arising during the pandemic, such as local government policies and socioeconomic impacts. © 2020 IEEE.

Analysis of public complaint reports during the COVID-19 pandemic: A case study of Jakarta's citizen relations management

COVID-19, as a newly discovered disease, has suddenly become a major challenging problem for many cities in the world, including Jakarta. This crisis affects all segments in day to day life. Jakarta citizens can report any problems related to COVID-19 through Citizen Relations Management, later known as CRM. This CRM system consists of 14 official complaint platforms that can be used by the citizens to report their complaints to the government and its working units. As a bridge between people and the government, this system is a form of problem-solving innovation. In this study, Exploratory Data Analysis (EDA) was carried out to analyze the pattern of complaint reports concerning COVID-19. The dataset used is CRM report data and daily COVID-19 positive case data. Through the analysis, a linkage is found between the cumulative number of reports related to COVID-19 and the cumulative number of COVID-19 positive cases. During the crisis period like the unfolding COVID-19 pandemic, transparency of information and citizen feedback can make invaluable contributions to an effective national response. This study is expected to encourage CRM to deal with the impact of COVID-19 effectively through the official platforms managed by Jakarta Smart City. © 2020 IEEE.

A study on the anxiety level of people related to the COVID-19 pandemic death cases: A case study of Jabodetabek region

Since its first case appeared in Depok, West Java, COVID-19 had sent people into panic and anxious apprehension. This paper aims to perceive the anxiety level of people related to the deaths caused by the COVID-19 in Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi). Factors such as socioeconomic vulnerability and human cognitive level (regarding COVID-19) are also perceived to play important roles in causing such anxiety. A total of 554 respondents have participated in this study. Results showed that respondents had a low level of death anxiety, remembering, and understanding cognitive levels, but had a high level of concern regarding their ability to fulfil their food needs and adequate healthcare access. © 2020 IEEE.

Developing the government COVID-19 website: Lessons Learned from Jakarta

Almost all countries obtain significant and multidimensional challenges of COVID-19. Various countries possess varied responses and policies regarding COVID-19. Since the Indonesian government affirmed COVID-19 a national emergency on March 2, 2020, it is necessary to have official information that can be accessed by the public, which at that time did not yet have the Central Government Website. Moreover, the importance of the availability of public information/data contained in official online pages can be used by governments to formulate data-based policies. Jakarta is a pioneer in developing a government website related to COVID-19. This paper provides lessons learned from developing an official COVID-19 website of the Provincial Government of Jakarta. This paper outlines different aspects of developing an official COVID-19 website and an ideal solution to the challenges involved in developing one. This paper uses agile development methods as an evidence base to develop a website. The most interesting finding is that the corona website has been successful in attaining 27,569,404 visitors, 120 collaborators who donate 151,567 pcs of social aid. This finding confirms that this study provides a better understanding of common elements in building an official COVID-19 website. The no-nonsense method of developing an official COVID-19 website can be easily replicated and followed by other cities to consider the model in developing a similar website. © 2020 IEEE.

Towards a collaborative framework for the large-scale social collaboration: A case of Jakarta's response to the Covid-19 pandemic

Covid-19 pandemic has driven many governments to discover solutions for various problems faced during the pandemic, including the Regional Government of Jakarta (GOJ). In light of such crisis and pandemic situation, there have been a group of people that need additional primary food supply. In contrast, wealthy people are keen to help the vulnerable. Several attempts have been made to distribute social aids to residents with financial difficulties. However, the nature of social welfare services remains unclear and unscheduled. In this paper, we present a framework for a collaborative digital platform as a hub for the people in need and potential contributors. Such a framework calls as Large-scale Social Collaboration (KSBB) for better aid distribution management. The KSBB framework serves as a basis for: (1) analyzing need assessment of individuals/communities in need;(2) mapping of targeted individuals/communities in need;and (3) facilitating coordination with local communities. This paper shows that strong policy mandate, binding institution, and professional information technology support in government is crucial for the deliverable. The program can be implemented with the role of the government as platform providers rather than executors of social donation programs. The real applicability of the framework is demonstrated in the paper through the case of Jakarta during the first Large-scale Social Restrictions (PSBB). The use of such a framework can also inspire other initiatives to consider the model in developing similar and related programs. © 2020 IEEE.

Building a smart city 4.0 ecosystem platform: An overview and case study

This paper presents an overview of a smart city 4.0 framework in accelerating digital transformation, especially during the COVID-19 pandemic, using Jakarta as a case study. The findings of this study provide new insights how to translate a vision into a reality in the form of smart city 4.0 framework that offers a significant opportunity to advance the understanding of building a smart city ecosystem with technologies, innovations and collaborations. This paper applies four principles of the framework, namely mobile-first, system-and-data driven, digital experience, and smart collaboration in building a smart city 4.0 ecosystem platform. Part of the aim of this paper is to examine Jakarta's super-app called JAKI that is compatible with such principles as a use case in the time of the pandemic. It provides a better understanding of common elements in building a new concept of a smart city. The results will inspire and give a contribution to other cities to consider the framework in building a smart city 4.0 ecosystem platform to foster quality of life, economic growth and sustainability. © 2020 IEEE.

Forecasting for a data-driven policy using time series methods in handling COVID-19 pandemic in Jakarta

The coronavirus diseases 2019 or COVID-19 has spread and infected millions of people around the world. The ongoing COVID-19 pandemic has taken an unprecedented toll on residents, business, commerce, and activity in many cities, including Jakarta, where there have been more than twelve thousand confirmed cases as of July 2020. The details of how COVID-19 spreads in Jakarta are still complicated and not completely understood because the number of infections is large and continues to climb. This paper conducts a quantitative analysis of the COVID-19 pandemic spreading using Jakarta as a case study for the evaluation and decision-making process. In this paper, time series models such as the Holt's exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) were used to forecast the number of COVID-19 cases in Jakarta between March 1 and July 6. Recently, data exploration and comparative analysis of time series models have been conducted to determine the optimal models for forecasting COVID-19 confirmed cases. The result shows that ARIMA has the highest R-Squared (R2), and lowest (Mean Squared Error) MSE and Root Mean Squared Error (RMSE) is the best model to forecast the upcoming number of infected cases of COVID-19 in Jakarta. Such a model shows promising results and fitting predictions in supporting data-driven policy in public health and epidemiology. © 2020 IEEE.