Big Data Analytics for Covid-19 Pandemic Prediction in Indonesia

Dewi Yanti Liliana

, Hata Maulana

and Agus Setiawan

Department of Informatics and Computer Engineering, Politeknik Negeri Jakarta, Depok, Indonesia

Keywords: Big Data, Covid-19, Data Visualization, Forecasting, Indonesia, Mitigation, Pandemic.

Abstract: The Covid-19 Pandemic has resulted a health crisis in the community. In Indonesia, up to May 28th 2020 it

was recorded that the total number of confirmed cases of Covid-19 reached 24.538 people. Indonesian

Government urgently needs a mitigation planning because this situation can lead to a long-term economic and

social crisis. As an effort to overcome the problems above, a prediction model of the Covid-19 pandemic

impact is needed for mitigation planning. This study aims to develop an analytical prediction model for the

Covid-19 pandemic in Indonesia. Big Data analytics and processing based on Machine Learning was applied

since Big Data technology can explore patterns in the data to see trends that can be used for visualization and

forecasting of the Covid-19 cases in Indonesia. SAP Analytics Cloud; a cloud-based Big data analytics

powerful software was used to build the prediction model. The results of this study recommended the

implementation of a Large-scale Social Restriction (LSR) to be applied continuously in impacted provinces

to suppress the transmission of Covid-19 in Indonesia.

1 INTRODUCTION

Since the beginning of 2020 the world has been

shaken by the Covid-19 virus pandemic which has

disrupted the stability of human life in all aspects. The

Covid-19 virus outbreak began in the city of Wuhan,

Hubei Province in China at the end of December 2019

(Sanche et al., 2020). After that, like a snowball

effect, it was growing bigger; almost all countries in

the world were exposed to the Covid-19 virus (WHO,

n.d.). Based on the current data in the Worldometer

website, Covid-19 victims in the world reached

5.730.736 people as per May 28, 2020 with 356.814

death tolls and 2.492.496 recovery cases

(Worldometer, n.d.). Whereas in Indonesia, it was

recorded that up to 28 May 2020 the total number of

positive cases of Covid-19 reached 24.538 people, the

number of recovered patients 6.240 people, and the

number of mortal patients 1.496 people. The chart in

Figure 1 shows the situation of the Covid-19 cases in

Indonesia until 28 May 2020 (katadata, n.d.).

The chart in figure 1 shows an increasing number

of cases every day. This chart describes an

exponential growth which means the quantity of

Covid-19 cases will rise over time. The visualization

https://orcid.org/0000-0002-9574-703X

https://orcid.org/0000-0002-2893-2972

from this graph is useful to describe the cases in

Indonesia.

Figure 1: Covid-19 cases in Indonesia.

By the time this paper is written, Indonesia is still

facing the increasing number of cases and nobody is

quite sure when the pandemic will end. Thus, it needs

a forecasting technique to give a foresight regarding

Covid-19 cases in Indonesia in order to arrange the

future action or mitigation planning from the

governments. The same thing was done by other

countries such as Italy; forecasting result was used to

Liliana, D., Maulana, H. and Setiawan, A.

Big Data Analytics for Covid-19 Pandemic Prediction in Indonesia.

DOI: 10.5220/0010962400003260

In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 1203-1209

ISBN: 978-989-758-615-6; ISSN: 2975-8246

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

1203

response as well as to set priorities to Covid-19 crisis

(Grasselli et al., 2020).

Forecasting can be used as a prediction tool of the

future cases. It can be used as an effort to provide an

initial response in handling Covid-19 cases in

Indonesia as well. In (Rustam et al., 2020) forecasting

has powerfully been applied based on machine

learning methods for Covid-19 cases using John

Hopkins dataset (Petropoulos & Makridakis, 2020).

Machine learning works by combining statistical

techniques and Big Data analytics to yield the

knowledge about future; or so-called data-driven

knowledge (Frické, 2018).

In the Big Data field, the term "analysis" is

different from "analytics". The fundamental

difference is that an analysis is carried out to find the

information from the available data to explain

patterns that occurred in the past that are useful for

current decision making. While analytics is an

activity to find patterns in the data and interpret the

knowledge of the patterns to predict something that

will happen in the future (Frické, 2018). In other

countries, e.g. China, Singapore and Canada, the use

of Big Data and Artificial Intelligence played an

important role in Covid-19 for deciding action

planning and mitigation. In contrast, in Indonesia the

information presented was mostly the analysis of

daily data and the aggregate data in the form of

statistical s and percentages related to Covid-19

cases. As a matter of facts, not many prediction

models have been produced because it required

forecasting methods and pattern recognition from

time-series data based on Artificial Intelligence and

Machine Learning methods.

Therefore, this paper proposed a Big Data

analytics for predicting the future cases of Covid-19

in Indonesia based on machine learning methods. Our

contribution is creating the prediction model of

Covid-19 cases in Indonesia using Big Data analytic

tools. This contribution is important since the Covid-

19 pandemic curve has not been flattening up to now,

therefore all efforts, including scientific effort must

be made in supporting all decisions related to Covid-

19 cases mitigation planning and handling. We

organized this paper systematically to provide a

comprehensive picture about the Covid-19 problems

in Indonesia; the proposed prediction methods; and

the discussion of the results.

2 COVID-19 GLOBAL PANDEMIC

2.1 Covid-19

Corona virus disease that first appears in the year of

2019; or in short Covid-19 is an infectious disease

caused by The Corona Virus type 2 (SARS-CoV-2)

and resulting an acute respiratory syndrome (Sanche

et al., 2020). Common symptoms caused by this

disease are fever, coughing, and shortness of breath.

Other symptoms include fatigue, muscle aches,

diarrhoea, sore throat, loss of sense of smell, and

stomach pain. Meanwhile some cases are reported to

cause mild symptoms that develop into severe

symptoms quickly, including pneumonia and multi-

organ failure if the patient is included in the comorbid

group (Radulescu & Cavanagh, 2020). Comorbid is a

group of patients with a risk of severe symptoms

because they have a history of congenital diseases

such as: diabetes, hypertension, heart disease,

pregnant women, and smokers. As per May 28

2020,

more than 5.7 million cases had been reported in more

than 200 countries and regions, this disease also

caused more than 350 thousand of deaths and more

than two million people recovered (Worldometer,

n.d.). Covid-19 has been proclaimed as a global

pandemic by WHO on March 2020.

The Covid-19 virus is spreading from a close

contact by small droplets produced when an infected

person coughs, sneezes or talks (Sanche et al., 2020).

These tiny drops are also produced when breathing,

but quickly fall to the ground or surface and generally

do not spread over long distances. People can also be

infected by touching a contaminated surface then

holding their face. This virus can survive on the

surface for up to 72 hours. The disease is most

contagious during the first three days after the

occurrence of symptoms, although infections may

occur before symptoms appear. The time from the

exposure to the occurrence of symptoms is usually

around 5 days, but can range from 2 up to 14 days

(Radulescu & Cavanagh, 2020). The standard method

for diagnosing Covid-19 is by applying the Reverse

Transcriptional Polymerase Chain Reaction (RRT-

PCR) in real-time from the nasopharyngeal swab,

which is taking samples from the lining of the nose

and throat (Long et al., 2020). Infections can also be

diagnosed from a combination of symptoms,

comorbid risk factors, and chest CT scans that show

symptoms of pneumonia.

Researchers in the UK and Germany found that

the Covid-19 virus has mutated into three types. They

labelled the Covid-19 virus with types A, B, and C.

The type A virus is the earliest type found in Wuhan

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and infects a lot in America and Australia. Type B

virus is found in East Asia. Whereas type C virus is a

child of type B virus that infects many people in

Europe and South East Asia (Forster et al., 2020).

Figure 2: Phylogenetic Network of Covid-19.

Source (Forster et al., 2020).

Figure 2 depicts Covid-19 phylogenetic network in

the world. In addition to its spread and mutation,

world researchers are also trying to develop a Covid-

19 vaccine to help human to overcome a pandemic

crisis (Forster et al., 2020).

2.2 Covid-19 Pandemic Impacts in

Indonesia

The impact of the Covid-19 outbreak can be seen in

almost all sectors in Indonesian people's lives. Social

activities have been banned and suspended

temporarily since the middle of March 2020.

Economic slowdown caused by transportation

services which were being reduced and tightly

regulated, tourism was closed, shopping centers were

empty from visitors and closed to the informal sector

such as online motorbike taxi, public transportation

drivers, street vendors, traveling merchants, MSMEs,

and labors/workers. Trade centers such as malls and

market which were usually crowded by people

suddenly deserted and closed temporarily. The

tourism sector has decreased; impacted by the

government regulation which closed tourist

attractions and entertainment venues. Work and study

were done at home with an online mode. Disruption

were in all human being live aspects.

The death rate due to Covid-19 in Indonesia was

the highest in Asia after China. The percentage of

death was 9.11%, the number of cases of corona virus

reached 1,986 cases with 134 people recovering.

From 3 to 5 April 2020 the number of confirmed

victims of Covid-19 was 2,273; where 164 recovered

and 198 died (katadata, n.d.). Fast responses and

accurate regulations are needed to suppress and

eradicate Covid-19 cases in Indonesia so that the

nation get out of from the crisis situation.

One of the efforts of the Indonesian government

to reduce the number of Covid-19 cases was by

implementing Large-scale Social Restrictions (LSR)

or known as PSBB in Indonesia; beside school

closure, workplace closure, and travel ban. The

implementation of LSR in most affected regions was

considered very urgent as the spread of Covid-19 was

also moving exponentially. The government assumed

that LSR was able to curb the increase of Covid-19

cases. But in the capital city Jakarta which has entered

the second phase of the LSR implementation, on

Wednesday May 6

2020 still found 4.709 positive

cases of Covid-19. That number increased compared

to the data on May 5

2020 which reached 4.687

cases. Hence, the LSR implementation must be

evaluated periodically to monitor its effectiveness.

3 BIG DATA ANALYTICS

In this section we will explain our research methods,

including Big data analytics for Covid-19 Crisis and

our preliminary work using Machine Learning

technique for Covid-19 prediction.

3.1 Big Data Analytics for Covid-19

Big Data is a large and complex collection of data

from various data sources. Big Data has 4V

characteristics which stand for Volume, Variety,

Velocity, and Veracity (Frické, 2018). Volume relates

to the size of data that reaches hundreds of Terabytes

per day and in a cloud storage. Variety relates to data

types which are also varying; not only from structured

databases but also from unstructured and semi-

structured data from internet media, social media, and

IoT sensors. Velocity means the data speed is very

fast; even in a real-time or relevant time span. Last,

veracity indicates that in big data, the data itself can

be valid but can also be disruptive and inaccurate.

Hence, the veracity aspect of big data to produce

knowledge must be considered.

Big Data technology involves analytic processing

(Frické, 2018). Analytic is the process of discovery,

interpretation, and communication of meaningful

patterns in data. There are four processes in analytic

activity as illustrated in figure 3. The analytic

processes include four activities: descriptive (what

happened), diagnostic (why did it happen), predictive

(what will happen), and prescriptive (how can we

Big Data Analytics for Covid-19 Pandemic Prediction in Indonesia

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make it happen). Big data technology produces

patterns or knowledge from data (data-driven

knowledge) known as value or insight. Value is the

highest form of data transformation (Frické, 2018).

Figure 3: Types of analytic processes in Big Data.

Big Data analytic can be applied to discover

knowledge for Covid-19 cases Indonesia. Some tasks

can be developed i.e.

• Forecasting for the continuous data prediction

related to Covid-19 using the Regression

method.

• Clustering for the description dan visualization

of data patterns related to Covid-19

• Classification for the prediction of categories

related to Covid-19 using the Machine Learning

methods

• Pattern Analysis to look for the relationship

between observed parameters using Sequential

Pattern Analysis.

In this paper, our proposed methods are describing the

first two techniques in Big Data (forecasting and

visualization). But we will explore all analytics

aspects in the near future.

3.2 Covid-19 Dataset

We used two sources of datasets: kawalCovid

(KawalCovid, n.d.) and databoks (katadata, n.d.).

KawalCovid dataset consisted of data from 511

regions in Indonesia. It provided a csv file data for

global Covid-19 cases; while databox provided daily

Covid-19 cases in Indonesia.

3.3 Preliminary Works

Our effort in developing big data analytics model was

started by doing preliminary works. Preliminary

research that has been done to obtain a prediction

model of the Covid-19 case in Indonesia was using

the regression technique on a time series data. The

prediction was done by using 55 days data taken from

(katadata, n.d.). The data started from the first case

recorded in Indonesia on March 2, 2020 until April

26, 2020. Covid-19 prediction chart was made by

using the exponential regression method. It visualized

the prediction chart of the dead and recovered victims

as in Figure 4.

Figure 4 shows two scatter diagrams in the form

of different dot colors that illustrate the number of

dead and recovered patients. With regression and

forecasting techniques, it is seen that on the 40th day

since March 2

2020 (or April 10

2020) there is an

increasing number of recovered patients with an

exponential rising trend. While the number of patients

dying will be more sloping, although not linearly but

predicted to be closer to the polynomial model which

means not an exponential. As an illustration, the type

of curve of the slowest growth rate in a row are linear

- polynomial - exponential.

Figure 4: Time series prediction of Covid-19 in Indonesia.

Furthermore, we employed Big data analytic tools

from SAP which is called SAP Analytics Cloud

(SAC) to perform the analytic task. SAC is a cloud-

based software as a service (SaaS) solution that has

analytics functionalities and visualization features.

Our exploration using SAC software was for: a)

visualization of Covid-19 cases in all provinces in

Indonesia; b) forecasting of Movement Control Order

(LSR) impact in Indonesia due to Covid-19

Pandemic.

4 RESULTS AND DISCUSSIONS

This part is divided into two subsections; a) the

visualization of big data analytics for Covid-19

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Pandemic in Indonesia; b) forecasting using Big data

analytics results.

4.1 Visualization Results

Big Data implementation using SAP Analytics Cloud

(SAC) software resulting three visualizations which

are called as Stories. Each story describes different

events and knowledges. The visualization results are

in Figure 5, Figure 6, and Figure 7.

Figure 5 is a result of SAC story which visualized

the comparison of the number of completed cases,

total cases of Covid-19 and the total population per

province in Indonesia. The dataset was KawalCovid

(KawalCovid, n.d.) recorded from the first case in

Indonesia on March, 2020 up to June, 2020. Three

objects in the chart are orange bar indicates total

cases; blue bar indicates completed cases; and green

line indicates the total population in each province of

Indonesia. The finding of Figure 5 can be seen that

although the province of Jawa Barat (West Java) has

the highest population among other provinces, the

number of completed cases has reached 122 out of a

total of 300 cases. This means that West Java is the

province with the highest level of completed case

handling with the ratio of 0.68.

Figure 6 is a result of SAC story which visualized

the comparison of the number of active cases,

recovery cases, and death cases per province in

Indonesia sorted by recovered cases. It used the same

dataset source as in Figure 5. In Figure 6, the yellow

area is an active case, green area is a recovery case,

and red area is a death case. Some findings are the

highest number of active, recovered and dead patients

ranked by the highest number of recovered cases was

in DKI Jakarta province, and the lowest was in Papua

Barat (West Papua). Jawa Timur (East Java) should

be a priority focus of the government because besides

active cases were high, death cases were also high;

indicated by the highest yellow line’s peak point as

well as the red line.

Figure 5. Visualization of Covid-19 cases and total number of populations in Indonesia.

Figure 6. Visualization of active, recovered, and dead Covid-19 cases in Indonesia

Big Data Analytics for Covid-19 Pandemic Prediction in Indonesia

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Figure 7 is a result of SAC insight which depicts

the confirmed case in Indonesia per month.

Figure 7: Monthly Covid-19 confirmed cases in Indonesia.

If we look at the chart in Figure 7, we can see that

the spread of Covid-19 cases in Indonesia has

increased dramatically from March to June. The

increase from March to April reached six times higher.

The increasing percentage every month from March to

June were 662%, 261%, and 156% respectively. Even

so, the highest number of spreads was in the last

month, where by mid-June it has increased up to

15,000 cases. If we look at the month before (May),

the number of infected people was increasing by

15.000 from May to June. Hence, we can roughly

predict that the month after will reach at least 15,000

as well. Therefore, necessary step is needed to reduce

this number.

4.2 Forecasting Results

Forecasting or prediction based on time series in

Covid-19 cases can be done by monitoring the number

of cases on a daily basis to observe the trend of cases

movement. The goal is to prevent the spread of the

Covid-19 in communities. We chose a region which

was most impacted in Indonesia which is Indonesia

Capital City, Jakarta. We kept track the time-series

Covid-19 cases data from the early outbreak in

Indonesia on March 2

up to June 20

,2020. The

source of the dataset was from Databox (katadata,

n.d.). Fig 8 depicts the forecasting results. We put two

vertical lines in the visualization chart which indicated

the starting point of LSR implementation phase 1 on

April 10

, 2020 and the end point of LSR phase 1 on

May 30

, 2020. After LSR phase 1, Indonesia moved

to a next phase of LSR with some relaxations in

several important sectors, such as economy and

trading. This phase is an adaptation step and well-

known as a new normal phase.

From Figure 8 we can learn that the LSR

implementation has effectively suppressed the

increasing rate of Covid-19 cases in the Jakarta region.

It can be seen from the flat-trend during the LSR

implementation. Whereas within less than a month

after the LSR phase 1 ended, we can see there is a

sudden increase in the Covid-19 cases during June.

The forecasting goes from mid-June until July 6

2020; or the shaded area. The forecasting area

predicted that there will be a significant increasing

during the new normal phase. Therefore, we suggest

the government to evaluate the new normal regulation

and strive for the implementation of LSR with

appropriate arrangements so that the rate of Covid-19

cases does not increase sharply but people still can

resume economic activities. Furthermore, we suggest

the increase implementation of LSR widely across

nation as well as increase the frequency of Covid-19

tests conducted in public area.

Figure 8: SAC forecasting result of Covid-19 in Jakarta City.

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5 CONCLUSIONS

The problems of the Covid-19 Pandemic resulted in a

health crisis followed by an economic and social

crisis in Indonesia. This paper aims to develop an

analytical prediction model for the Covid-19

pandemic in Indonesia. The prediction used Big Data

analytics technology. The results showed the

visualization of Covid-19 cases in Indonesia as well

as the trend which was still increasing. The spread of

the Covid-19 virus can be avoided by breaking the

chain of transmission and continuing to comply with

health protocols. The provincial government in

Indonesia is expected to be more vigorous and

decisive in enforcing large-scale social restrictions

(LSR) to break the chain of spread of Covid-19

infections. Our future works will be on exploring

other Big Data analytics using classification and

pattern recognition strategies.

ACKNOWLEDGEMENTS

This work was financially supported by Politeknik

Negeri Jakarta through The Distinguished Research

of Higher Education Institution Decentralization

Grant (PUPT).

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