Journal of Theoretical and Applied Information Technology

November 2021. Vol.99. No 22

ISSN:

1992-8645

www.jatit.org

E-ISSN:

1817-3195

5460

BIG DATA ANALYSIS ON YOUTUBE WITH TABLEAU

JOHANES FERNANDES ANDRY,

HENDY TANNADY,

ISABELLE IVANA LIMAWAL,

GLISINA DWINOOR REMBULAN,

RUSTONO FARADY MARTA

Department of Information System, Universitas Bunda Mulia, Jakarta, Indonesia 14430

Department of Management Graduate Program, Institut Teknologi dan Bisnis Kalbis, Jakarta, Indonesia

13210

Department of Information System, Universitas Bunda Mulia, Jakarta, Indonesia 14430

Department of Industrial Engineering, Universitas Bunda Mulia, Jakarta, Indonesia 14430

Master's Degree of Communication Science Department, Post-Graduate Studies, Universitas Bunda Mulia,

Jakarta, Indonesia, 14430

E-mail:

jandry@bundamulia.ac.id,

*hendytannady@gmail.com,

isabelleivana@gmail.com,

grembulan@bundamulia.ac.id,

rmarta@bundamulia.ac.id

ABSTRACT

YouTube is the second biggest search engine on the web and a platform with features where users can post,

view, comment, and link to videos. YouTube has more than a billion active users where users can see

Recommended Channels, which are based on videos that users watch frequently. Furthermore, Trending is

based on video trending on the number of clicks the video gets every day. This study aims to provide insight

into how understanding and implementation of Big Data on YouTube. In the current era, Big Data is gaining

much interest because of the opportunities and benefits felt to be unprecedented. Big data analytics can

leverage business change, enhance decision-making by applying advanced analytic techniques on big data,

and reveal hidden insights and valuable knowledge. To better understand the changes brought about by big

data, this paper focuses on data analysis using data visualization. In analyzing YouTube trending videos, we

use tableau software to help visualize the data that has been obtained. The following are some of the charts

used in this analysis, such as pie charts, area charts, horizontal bars, highlight tables, treemaps, and mapping.

Data Visualization can help YouTube, and its users identify which areas need to be improved, which factors

affect users' satisfaction and dissatisfaction, and what to do and know to develop parts lacking and pay

attention to today's users' needs. Visualized data can give a better prediction of current trending topics or

videos and future growth. It can be used to influence our decision-making.

Keywords:

Big Data, YouTube, Data Analytics, Data Visualization

INTRODUCTION

Social network data, audio, video in social media,

and social networking sites are ample data examples.

One example is YouTube. This platform was

founded in 2005 and developed rapidly to become

the most crucial video-sharing website on the web

[1]. Following its acquisition by Google, the location

has continued to grow in popularity. It is now one of

the top-visited sites globally and attracts 800 million

unique visitors per month [2]. Where users can

upload videos, watch various videos available on the

platform with various topics. This platform provides

various facilities to interact with one another and

answer certain video content. Using big data, users

can use the facilities provided, like users can see

Recommended Channels, where users can view

videos recommended supported videos that users

often watch. Also, there is Trending, where videos

that are being watched by many users are often seen

in Trending. There are also Recommended Videos,

which are videos that are recommended, supported

videos that users watch. YouTube has great potential

to succeed in potential consumers, media, and

interest groups. With numerous users using the

platform, indirectly creating new data are often

processed to supply a replacement analysis result. In

Internet usage, each user automatically collects large

amounts of knowledge a day. This data is stored

directly by the physical device. It is more feasible for

giant amounts of storage to store within the cloud

than a locally available hard disc. Because storing on

the hard disc, the danger of losing data is bigger than

storing within the cloud. Around 2016, active

internet users reached 3,424,971,237 worldwide.

Users of social sites or social media are increasing a

day and leading to data collection. Google and

YouTube have about 15 exabytes of knowledge, and

Facebook and Twitter have tons of knowledge

collection. The info collected is getting bigger and

Journal of Theoretical and Applied Information Technology

November 2021. Vol.99. No 22

ISSN:

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E-ISSN:

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larger because it has increasingly collected

everywhere - sensing mobile information devices,

antennas, sensory technology, software logs,

cameras, microphones, frequency identification

readers, and wireless sensor networks. Supported

world technology, information storage capacity has

doubled every 40 months since the 1980s; as of 2012.

A day the info has been generated about 2.5 trillion

[3].

Big data provides organizations with insights into

their business processes and enables them to market

their products better by employing analytical

techniques like data mining [4]. The method of

extracting valuable information and data from large

ones is named "Data Mining." The platform uses big

data technology to take care of its filing system. The

quantity of knowledge generated from the start of

our time until the last decade is perhaps generated

every few days today [5]. Altogether probability, this

trend is about to continue because of the increase in

social media applications. Simple access to data

through mobile devices exponentially increases the

quantity of knowledge captured every second [6].

The platform has billions of individuals who

connect, inform, and encourage the planet by making

videos a day. Therefore, it is not surprising that

YouTube now features a significant influence on the

web but has severe problems. Archiving, processing,

and efficient analysis of sizeable data is extremely

difficult. The info generated by the billions of

YouTube videos is appropriate. Consistent with

statistics published by Google, YouTube has quite

one billion customers. Also, as many as 33% of

individuals spend their time watching YouTube

because the platform has around 300 hours of video

a day and may even exceed the amount published by

Google [7]. Social media's exponential growth in

contemporary society makes them necessary tools

for communication, content creation, sharing, and

business growth [8].

The authors will analyze daily statistics for

trending videos where the dataset comes from

Kaggle.com. To work out which videos are trending

and are on the highest list this year, YouTube uses

various

factors,

including

measuring

user

engagement (trending dates, total videos, region, and

category). The info to be analyzed is within the USA,

France, Russia, India, and Japan, with up to 200

trending videos registered per day. Each region data

is during a separate file. Data includes trending

dates, total videos, region, and category. The authors

will visualize data that has been taken from

Kaggle.com.

Data visualization is the use of natural human

skills to enhance processing and organization

efficiency. Data visualization represents data or

information during a graphic, chart, or other visual

formats. Visualization can help us affect more

complex information and enhance memory [9]. It

communicates the relationships of the info with

images. This is often vital because it allows trends

and patterns to be more easily seen. With the increase

of massive data, we would like to interpret

increasingly larger batches of knowledge. Machine

learning makes it easier to conduct analyses like

predictive analysis, which may then function helpful

visualizations to present. In this sense, data

visualization is not only crucial for data scientists

and data analysts, but all people in any career got to

understand data visualization.

It is often used interchangeably with information

graphics,

statistical

charts,

and

knowledge

visualization. We want data visualization because

that visual summary information makes it easier to

spot patterns and trends than to look through the

thousands of rows on a spreadsheet. That is how the

human brain works. Since the aim of knowledge

analysis is to realize insight, data is far more valuable

when visualized. Albeit a knowledge analyst can pull

insights from data without visualization, it will be

tougher to speak the meaning without visualization

[10]. Data visualization may be a big field with many

disciplines.

precisely

due

this

interdisciplinary nature that the visualization field is

filled with vitality and opportunities. To research and

visualize the info, the authors are going to be using

Tableau Software.

Tableau is an end-to-end data analytics platform

that permits us to prep, analyze, collaborate, and

share our significant data insights. Tableau excels in

self-service visual analysis, allowing people to ask

new questions of governed big data and share those

insights across the organization [11]. Tableau works

closely with the leaders during this space to support

whatever platform the customer chooses. Tableau

allows users to seek out this value in company data

and investments within the technology to get the

most out of their data. Tableau can help businesses

see and understand Big Data. During this time,

efficient analysis of business data and their storage

on appropriate devices is tedious. The YouTube

videos and, therefore, the multimedia data, which are

generated from it, are usually unstructured. Perfect

analysis of those semi-structured and unstructured

multimedia data may be a big challenge. This study

aims to supply insight into how understanding and

implementation of massive Data on YouTube

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E-ISSN:

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LITERATURE REVIEW

This section will provide the foundation and

support for a new insight that authors contribute.

However, a literature review's main target is to

summarize and synthesize others' arguments and

ideas without adding original contributions.

2.1

Big Data

The term "Big Data" appeared for the primary

time in 1998 on the slide deck Silicon Graphics

(SGI) by John Mashey under the title "Big Data and

therefore the NextWave of InfraStress." The word

Big Data is very relevant from the start, as the first

book to mention "Big Data" is a mining data book

that appeared in 1998 by Weiss and Indrukya. For

the first time, an academic paper with the inscription

"Big data" in the title appeared sometime later in

2000 in a Newspaper by Diebold. The origin of the

term "Big Data" is that we are currently creating a

file of data every day. Big data can also be

interpreted as data sets of sizes beyond ordinary

software capabilities used to capture, curate,

manage, and process data in considerable time [12].

Big data is somewhat unusual in that it was broadly

accepted in the commercial and public space before

the academic discourse has had time to catch up [13].

This may explain why most of the literature on big

data has increased during the past few years.

Big data is not new, but the rapid rate of adoption

in recent times may make it appear so [14]. Sensors,

mobiles, and social media networks are just a couple

of samples of modern digital technologies that have

penetrated our daily lives. An excessive amount of

digital data is being generated a day. Nowadays, an

increasing number of knowledge silos are created

globally, suggesting that this growth will never stop.

These data are not only voluminous but also

continuous, streaming, real-time, dynamic, and

volatile [15]. Data have grown exponentially in

"volume", variety' and "velocity" [16], [17]. This is

generally described by the "3 V" of big data (volume,

variety, and velocity), which are used to describe this

phenomenon. Literature indicates that big data can

unlock plenty of new opportunities and deliver

operational and financial value [18], [19]. Internet

advent has triggered a boom in information research.

Companies are flooded by the wealth of knowledge

that results from simple internet browsing.

2.2

YouTube

YouTube was founded in 2005 and developed the

platform rapidly to become the largest video-sharing

website on the internet [1]. Following its acquisition

by Google, the location has continued to grow in

popularity and now attracts almost half a billion

unique users per month. The location allows users to

upload a vast number of video clips, which may be

viewed and linked to anyone. YouTube provides

various facilities to permit registered users to interact

with one another and share their content responses.

One option is for users to post direct video responses

to what they need to be viewed. Other options are

afforded by the user profiles, or "channels." There

are personal messaging service and a more visible

commenting option on the profile, and users can

prefer to store and display lists of their favorite

videos.

They can also befriend other users on the site or

become "fans" by subscribing to receive alerts once

they post new material. The location provides

sharing, rating, and commenting options on

individual videos. The one that has uploaded the

video can change the comment settings. Only certain

users can comment and need that comments are

subject to moderation before they become publicly

visible or prefer to disable the comments facility

altogether. Users can "rate" comments using thumbs-

up and thumbs-down buttons, as they will for videos.

The comments that gather the foremost significant

number of positive votes are displayed above the

most bulk of comments. The comments facility was

intended to be "a section of text for users to supply

information associated with a video" and where

users could express their opinion on the video.

However, in practice, this is often not always what

happens. Technology provides affordances but is not

prescriptive; they have often put to uses aside from

that that it had been initially intended. This is often

the case with the comments facility: a user can write

things within the comments box that are not relevant

to the video [2].

2.3

Data Visualization

With the advent of technology, YouTube ought to

deal with numerous rows of data. To make a better

judgment, the data must be analyzed. When a large

amount of data is displayed to an audience dealing

with it, daunting and minor trends might always skip

the analyst's eye. Data visualization is a process

wherein the analyzed data is represented visually in

graphs and pictures, both attractive and functional.

Data is one among the foremost powerful and – often

overlooked – tools to speak a message. However,

those numbers alone would not necessarily make the

desired impact if the audience cannot interpret them.

That is why data visualization is the key [20].

Interactive charts and figures help understand the

varied outcomes possible in each situation. This type

of visualization helps people add an extra dimension

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November 2021. Vol.99. No 22

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to their presentations, and cohesively display

essential statistics.

Tableau is a business intelligence and data

visualization tool that helps chart out the data onto

graphs, maps, etc. and break it down into several

components on a dashboard for real-time analysis.

With the help of Tableau, YouTube can explore and

analyses data visually. The program also offers the

option to create a dynamic presentation on the app

itself; post data visualization sheets have been

generated. By presenting the data in a visually

engaging way, one can enhance one's own and the

audience's experience. Tableau quickly creates

graphs and visualizations that include generic filters,

using graphs as filters; tooltips (hover over pop-ups);

and embedded webpages. A Tableau data extract

will take a snapshot of our data and put it into the

Tableau format that makes it easy for the software to

compute. This comes in handy when one has a large

data set. Data visualization is not just the process of

creating a few charts. It is about helping the audience

understand the significance of the data that is being

presented. Tableau makes the data easier to

comprehend,

making

statistical

relationships,

patterns, and critical trends more exposed and

recognized [21].

METHODOLOGY

In this section, we will discuss the step-by-step

process,

which

helped

achieve

this

paper's

completion.

Figure 1: Research Flowchart[22]

Identify The Problem

The initial stage of research is to identify

existing problems. The authors want to analyze

and visualize the Trending YouTube Videos

dataset, where the data are taken in the USA,

Great Britain, South Korea, and Japan.

Determine The Method To Be Used

After the problem has been identified, the

authors then determine the method that will be

used. The method that will be used is data

visualization.

Data

visualization

visually

represents quantitative data with or without axes

in schematic or diagrammatic forms, e.g., Table,

Line chart, Pie chart, Histogram, Scatter plot, etc.

[23].

Data Collection

After determining the appropriate method, the

authors then collected the data. The authors used

the Trending YouTube Videos dataset, where the

dataset is taken from Kaggle.com.

Data Selection

Next is the data selection stage. At this stage,

the data will be selected. The input variables

include video titles, channel titles, published

times, tags, views, likes and dislikes, descriptions,

and the number of comments.

Data processing

After all the necessary data have been selected,

the next stage is data processing. At this stage, the

data will be processed using Tableau, where the

data will be transformed or change the data

attribute values into the appropriate data so that

the data can be processed using data visualization.

The complete dataset will be obtained, which is

used to process to the next stage [24].

Testing Data

At this stage, the data will be tested where data

testing is a stage that determines whether the test

made is suitable for use or not. If the test has

produced an output that suits the authors' needs,

the authors can proceed to the next stage.

However, if the test has not produced an output

that matches the needs, we have to return to the

data processing stage; if it has entered the repeat

or failure stage, it is necessary to solve it by

analyzing data processing [25].

Conclusion

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This stage is the last stage where the authors

draw conclusions that refer to the formulation of

the problem and research objectives based on the

test results. Suggestions are used to develop

further research and are included to improve the

quality of research.

RESULT AND ANALYSIS

Before using the Tableau application, make

sure the data to be processed first. The data chosen

is data from YouTube Trending Videos in the

USA, France, Russia, India, and Japan from 2017

to 2018. When all data is valid, data processing

can be done using the Tableau application. The

first step is to connect the data. Open Tableau

Desktop, and after that, we will choose a select

connector, which is what data we will use.

Because the data taken from Kaggle is CSV, we

choose a text file; after that, we will be directed to

choose what data to use. After the data has been

selected, we will enter the Data Source. In the data

source, we can see the dataset that has been taken

from Kaggle because we are using multiple data

sources; we can see them all listed in the

data

source. After that, we can combine the data we

want to retrieve, namely the USA, France, Russia,

India, and Japan.

After combining the data, use a data interpreter

to help clean up the CSV data that has been

retrieved. After that, we can select some of the

data needed for data visualization. Data can be

classified into two parts, namely dimension or

measure. These data are data that have been

selected and processed to produce the data

visualization previously described. In Table 1, we

can see an explanation of the data that we used.

After the data has been selected, the data will be

processed.

Table 1: Type of Data

Trending Date is the date a video is

being considered attractive by

many YouTube viewers/users.

Total Videos

The total number of videos

uploaded to YouTube by users.

Figure 2: Trending History

Next, we will go to sheet 1, where we will

visualize the existing data. The dataset taken from

Kaggle starts from 14 November 2017 to 14 June

2018. After that, we will explain the results of the

data

visualization

research

that

have

examined. As can be seen in Figure 2, the chart is

an area chart. An area chart is a line chart in which

the area between the lines and axes is shaded

according to the part being represented. These

charts are typically wont to represent accumulated

totals over time and are the traditional thanks to

displaying stacked lines. The area chart may be a

combination between a line graph and a stacked

bar graph. By stacking the quantity beneath the

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November 2021. Vol.99. No 22

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road, the chart shows the entire fields as their

relative size to every other.

Based on the results of the data visualization in

Figure 2, it can be seen that the Autos & Vehicles

category is the category with the highest number

of videos, and the Travel & Events category is the

category with the least number of videos. This

shows that YouTube users are more interested in

videos in the Autos & Vehicles category than

other categories.

Figure 3: Trending by country

Next in Figure 3 is a pie chart. The pie chart

represents data as slices of a circle with different

sizes and colors. The slices are labeled, and

therefore the numbers like each slice are

additionally represented within the chart. We will

select the chart option from the Marks card to

make a chart. It can be seen in Figure 3 that the

highest number of trending videos came from the

USA region, amounting to 80,758. A total percent

of 23.75%, followed by France with a total

number of trending videos of 75,404 and a total

percent of 22.18%, Russia with a total number of

trending video 73,694 and a total percent of

21.68% India with a total trending video of 73,372

and a total percent of 21.58% Japan with a total

number of video trending 36,762 and a total

percent of 10.81%.

Based on the horizontal bars in Figure 4, it can

be seen that total trending videos in 2017 came

from the USA with 18,918, followed by France

with 17,760 videos, India with 17,706 videos,

Russia with 17,276 videos. Furthermore, it can be

seen on the trending date for 2018 that the USA

still occupies the first position for the highest

number of trending videos with a total number of

videos of 61,840, followed by France with a total

of 57,644 videos, Russia with a total of 56,418

videos, India with a total of 55,666 videos, and

finally Japan with a total of 36,762 videos.

Figure 4: Trending date by country

Figure 5 shows the highlight tables that show

the total trending videos by category and country.

The highest number of trending videos uploaded

comes from India and is in the Entertainment

category, amounting to 32,924 videos. Based on

the category, it can be seen that the total videos

from the five regions that attracted the most users'

interest

were

the

Entertainment

category.

Whereas previously mentioned, India had 32,924

videos, followed by USA 19,638 videos, France

19,020 videos, Japan 11,734 videos, and Russia

11,692 videos. Furthermore, it can be seen that the

Trailers category, with a total of 4 trending videos,

is the category that attracts YouTube users the

least.

Figure 5: Trending by Country and Category

Next, we will analyze our data geographically.

With map visualization, it is easier for us to see

our data. To create map visualization, we first

need to change Region to Latitude and Longitude

geographic roles. After that, we will enter

Longitude in columns and Latitude in rows. To

make it easier to view the map, each region's

colors can be changed

so that each region's colors

differ. In Figure 6, we can see the mapping of the

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USA, France, Russia, India, and Japan regions

with the total number of trending videos from

2017 to 2018.

Figure 6: Mapping by Country

Figure 7 shows a treemap chart. Treemap in

Tableau may be a primary chart type that's

represented by nested rectangular boxes. This

chart is often useful for giant

datasets for

visualization.

This

graph

will

mark

the

hierarchical data for comparative analysis and a

crucial chart to research the data set's anomalies.

In addition to the scatter plot, treemaps are only

visualization types that reasonably allow us to

communicate and consume many marks on one

view. This makes it easier to identify patterns and

relationships that we would not rather be ready to

see. Colors and gradients are wont to group items

while still identifying individual items. This chart

works rather well for

a massive amount of

knowledge. When the info increases, it also

increases the matter of understanding the data.

Treemap charts are effective practices once we

want to speak and consume a fair number of

marks on one view. With this significant behavior,

a user can easily spot patterns and relationships

between them, otherwise impossible.

Figure 7: Videos Count by Category and Country

As shown in figure 7, the largest square size is

India, with the Entertainment category in 2018

with 25,156 videos trending. Meanwhile, for 2017

with the same country and category, there were

7,768 videos. Next is the USA in 2018 with the

same category as before, with 15,010 trending

videos. It can be seen that from 2017 to 2018, the

Entertainment category attracted the most interest

from YouTube users, and in 2018, the second

most popular category was the people & blogs

category. In 2017, the second most popular

category was the comedy category, followed by

the people & blogs category.

A dashboard is a collection of sheets, which we

can compare with various data simultaneously.

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After creating several data visualization sheets on

Tableau, we can combine the data visualizations

made on these sheets. As we can see in Figure 8,

we can see the full data visualization result

simultaneously.

CONCLUSION

In this paper, we have analyzed trending

YouTube videos data using data visualization.

As the "Age of Big Data" kicks into high gear,

visualization is an increasingly crucial tool to

make sense of the trillions of rows of data

generated every day. To better understand the

changes brought about by big data, this paper

focuses

data

analysis

using

data

visualization, where this data visualization we

are easier to understand through the visual

world, where pictures speak louder than words.

Data visualization is especially important for

big data and data analysis projects because it

allows trends and patterns to be more easily

seen. Good visuals tell a story, remove any

distractions from data, and highlight useful

information. Furthermore, since visualization

is so prolific, it is also one of the most useful

professional skills to develop.

With the rise of big data upon us, we need to

interpret increasingly larger batches of data.

Nevertheless, data visualization is not only

crucial for data scientists and data analysts; it is

necessary to understand data visualization in

any career. Whether we work in finance,

marketing, tech, design, or anything else, we

need to visualize data. That fact showcases the

importance of data visualization. Complex

algorithms are easier to understand in a data

visualization format than lines and text lines

and numbers. With so much information being

collected through data analysis in the business

world today, we must have a way to paint a

picture of that data to interpret it. If we are a

YouTuber, we will indirectly think about how

our videos can be trending and what type of

videos

trending

among

users.

For

any

YouTubers who question this, we can use data

visualization in answering these questions.

In analyzing YouTube trending videos, we use

tableau software to help visualize the data that has

been obtained. The following are some of the

charts used in this analysis, such as pie charts, area

charts, horizontal bars, highlight tables, treemaps,

and mapping. Based on the results of the analysis

that has been done, we can conclude that the

Entertainment category is the category that most

attracts YouTube users. This can be proven in

Figures 5 and 7, where from 2017 to 2018, the

entertainment category was the category with the

most trending videos compared to other

categories. Furthermore, it can also be seen that

the number of trending videos in 2017 was

significantly less than in 2018. In 2018 trending

videos increased dramatically, and with this

comparison, it can be concluded that YouTube

users in 2018 also increased.

In 2017 the categories that received much

attention from users were entertainment, people &

blogs, news & politics, comedy, music, and film

& animation. Simultaneously, the less attractive

categories to users are the categories of movies,

trailers, nonprofits & activism, shows, travels &

events. In 2018 the categories that received much

attention from users were entertainment, people &

blogs, music, news & politics, comedy, and

sports. As we can see in Figure 3 Trending by

Country, it can be concluded that the most

trending videos are from the USA, followed by

France and Russia.

With this analysis results, Data Visualization

can help YouTube and its users identify which

areas need to be improved, which factors affect

users' satisfaction and dissatisfaction, and what

to do and know to develop parts lacking and pay

attention to today's users' needs. Visualized

data can give YouTube and its users a better

prediction of current trending topics or videos

and future growth. It can be used to influence

our decision-making. It is especially important

when analyzing and implementing strategies to

improve YouTube; visualization makes it easy

to ensure any platform is as optimized as

possible. We need data visualization because

the human brain is not well equipped to devour

such a lot of raw, unorganized information and

switch

into

something

usable

and

understandable. We want graphs and charts to

speak data findings to spot patterns and trends

to realize insight and make better decisions

faster.

The problem lies in too much data that is

difficult to analyze. In this paper, we suggest

using data visualization in analyzing data to

make it easier to read and understand. By

applying such analytics to big data, valuable

information can be extracted and exploited to

improve

decision-making

and

support

informed decisions. Finally, if appropriately

implemented, any new technology can bring

with it some potential benefits and innovations,

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let alone big data, which is an extraordinary

field with a bright future if approached

correctly. We believe that big data analytics is

essential in this era of data glut and can provide

unexpected insights and benefits for decision-

makers

in various

fields.

If adequately

exploited and applied, big data analytics can

provide a basis for progress at the scientific,

technological, and human levels.

REFERENCES

[1]

P. Gill, M. Arlitt, Z. Li, and A. Mahanti,

“YouTube traffic characterization: A view

from

the

edge,”

2007,

doi:

10.1145/1298306.1298310.

[2]

A. Madden, I. Ruthven, and D. McMenemy,

“A

classification

scheme

for

content

analyses of YouTube video comments,”

Doc.

, 2013, doi: 10.1108/JD-06-2012-0078.

[3]

F. E. Gunawan, J. F. Andry, H. Tannady and R.

Meylovsky,

“Designing

Enterprise

Architecture Using TOGAF Framework in

Meteorological,

Climatological,

and

Geophysical Agency,”

Journal of Theoretical

and Applied Information Technology

, vol. 97,

no. 20, pp. 2376-2385. 2019.

[4]

E. W. T. Ngai, L. Xiu, and D. C. K. Chau,

“Application of data mining techniques in

customer

relationship

management:

literature review and classification,”

Expert

Systems

with

Applications

2009,

doi:10.1016/j.eswa.2008.02.021.

[5]

J. F. Andry, H. Tannady and F. E. Gunawan,

“Purchase Order Information System Using

Feature Driven Development Methodology,”

International Journal of Advanced Trends in

Computer Science and Engineering,

vol. 9, no.

2, pp. 1107-1112

[6]

D. Korte, “Business Intelligence in the

Hospitality Industry,”

Int. J. Innov. Manag.

Technol.

2013,

doi:

10.7763/ijimt.2013.v4.435.

[7]

L. C. S. Reddy and Dr. D. Murali,

“YouTube: big data analytics using Hadoop

and map reduce.,”

Int. J. Eng. Technol.

, vol.

7, pp. 12–15, 2018.\

[8]

H. Tannady, J. F. Andry, F. E. Gunawan and J.

Mayseleste, “Enterprise Architecture Artifacts

Enablers for IT Strategy and Business

Alignment

Forwarding

Services,”

International Journal of Advanced Trends in

Computer Science and Engineering,

vol. 9, no.

2, pp. 1465-1472

[9]

E. D. Madyatmadja, L. Liliana, J. F. Andry and

H. Tannady, “Risk Analysis of Human

Resource Information Systems Using Cobit 5,”

Journal

Theoretical

and

Applied

Information Technology

, vol. 98, no. 21, pp.

3357-3367

[10]

Nikhat Akhtar, Nazia Tabassum, Asif

Perwej, and Yusuf Perwej, “Data analytics

and visualization using Tableau utilitarian

for COVID-19 (Coronavirus),”

Glob. J. Eng.

Technol.

Adv.

2020,

doi:

10.30574/gjeta.2020.3.2.0029.

[11]

“Big Data Analytics: Transform terabytes

into

insights.”

https://www.tableau.com/learn/articles/big-

data-anal ytics#:~:text=Tableau is an end-

to,those insights across the organization.

[12]

J. R. Mashey,

Big Data and the Next Wave of

InfraStress

Problems,

Solutions,

Opportunities

. 1998.

[13]

A. Gandomi and M. Haider, “Beyond the

hype: Big data concepts, methods, and

analytics,”

Int.

Inf.

Manage.

2015,doi:10.1016/j.ijinfomgt.2014.10.007.

[14]

J. F. Andry, H. Tannady and F. Nurprihatin,

“

Eliciting Requirements of Order Fulfillment

in A Company

,” The 2

International

Conference on Engineering and Applied

Sciences

2019

(InCEAS

2019),

IOP

Conference Series: Materials Science and

Engineering

[15]

J. L. Monino and S. Sedkaoui,

Big Data,

Open Data and Data Development

. 2016.

[16]

S. V. K. Jagadish

et al.

, “Genetic Advances

in Adapting Rice to a Rapidly Changing

Climate,”

Journal of Agronomy and Crop

Science

2012,

doi:

10.1111/j.1439-

037X.2012.00525.x.

[17]

A. McAfee and E. Brynjolfsson, “Big data:

The management revolution,”

Harv. Bus.

Rev.

, 2012.

[18]

F. J. Ohlhorst,

Big Data Analytics: Turning

Big Data into Big Money

. 2012.

[19]

Resdiansyah, J. Darmawan, A. H. Wijaya, L.

Hakim and H. Tannady, “

Comparing Freeman

Chain Code 4 Adjacency Algorithm and LZMA

Algorithm in Binary Image Compression,”

Annual

Conference

Science

and

Technology Research (ACOSTER) 2020,

Journal of Physics: Conference Series

[20]

Hubspot & Visage, “How To Use Data

Visualization To Win Over Your Audience.”

[21]

Ashish,

Varma

Sakshi,

“Data

Journal of Theoretical and Applied Information Technology

November 2021. Vol.99. No 22

ISSN:

1992-8645

www.jatit.org

E-ISSN:

1817-3195

5469

Visualization: Using Tableau for Decision

Making,”

Res. Rev. Int. J. Multidiscip.

, vol.

4, no. 4, 2019.

[22]

P. Pathak, S. Gupta, and G. S. Dangayach,

“Sustainable Waste Management: A Case

From Indian Cement Industry,” Brazilian J.

Oper.

Prod.

Manag.,

2015,

doi:

10.14488/bjopm.2015.v12.n2.a7.

[23]

S. Sara Banu, D. Uma, M. Waseem

Ashfaque, andQ. S. Ali Ahmed, “A review

on Visualization Approaches of Data mining

in heavy spatial databases,”

IOSR J. Comput.

Eng. Ver. V

, 2015, doi:10.9790/0661-

171595105.

[24]

M. Khan and S. Shah Khan, “Data and

Information Visualization Methods, and

Interactive Mechanisms: A Survey,” 2011.

[25]

A. S. Nagdive and D. R. M. Tugnayat,

“Overview

Performance

Testing

Approach in Big Data,”

Int. J. Adv. Res.

Comput. Sci.

, 2014.

YouTube Data Visualization

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