The Potential for Implementing a Big Data Analytic-based Smart Village in Indonesia

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The Potential for Implementing a Big Data
Analytic-based Smart Village in Indonesia
Eneng Tita Tosida
Computer Science Dept.
IPB University
Bogor, Indonesia
enengtitatosida@apps.ipb.ac.id
Yeni Herdiyeni
Computer Science Dept.
IPB University
Bogor, Indonesia
yeni.herdiyeni@apps.ipb.ac.id
Suprehatin Suprehatin
Agrobusiness Dept.
IPB University
Bogor, Indonesia
suprehatin@apps.ipb.ac.id
Marimin
Industrial Technology Dept.
IPB University
Bogor, Indonesia
mariimin@apps.ipb.ac.id
Abstract— Smart village is one of the solutions to reduce poverty
in rural areas. The main objective of this research is to map the
potential implementation of the concept of smart villages based
on big data analytics in Indonesia. This research was conducted
through the elaboration of text mining-based Systematic
Literature Review (SLR) with multiple regression analysis of
the 2018 Village Potential Data in Indonesia. The contribution
of this study is the production of a map describing the potential
for implementing smart villages based on big data analytics in
Indonesia. SLR cluster analysis produces a dendrogram that
maps the basic terminology of smart villages based on big data
analytic. Indonesia has quite substantial economic and social
capital resources, which has a positive effect on the poor and
farmers/fishermen (R2 = 0.9759 and 0.9482) in the villages. This
occurs through a mix of regional budget revenue (APBD) and
local self-subsistent (Swadaya) funding schemes in the
management of agricultural and non-agricultural small
businesses in the village. Indonesia also has sufficient capital for
managing information and communication technology (ICT) in
the village for the development of big data analytic smart
villages. There is a relatively strong influence on the poor and
farmers/fishermen (R2 = 0.5946 and 0.6006). Therefore, the
challenge for future research to develop a smart village model
based on big data analytics that is appropriate to the territory
of Indonesia. This model needs to be elaborated with diverse
factors including economic, social, cultural and smart
educational potential as well should include indicators of the
potential for data technology available on various media,
through the framework of agriculture big data analytic.
Keywords— Big data analytic, Clustering, Multiple regression
Smart village, Systematic Literature Review
I. INTRODUCTION
Indonesia is an archipelago and is strengthened by rural
areas in which a percentage of agricultural land use more than
urban areas. In 2018 the Central Bureau of Statistics (BPS)
reported that Indonesia had 74,517 villages. In addition, there
are 919 Nagari in West Sumatra, 8,444 subdistricts, and 51
Transmigration Settlement Units (UPT) in Indonesia.
Development disparities in cities and villages still occur in
Indonesia. This occurred due to factors influencing, the
increasingly massive urbanization process. In part, due to the
higher attractiveness of the city compared to the monotonous
village. The disparity in village development in Indonesia was
addressed in legislation with the issuing of the Village Law
No. 6 of 2014. The law places a priority on development
starting from in the village and the periphery. This law was
reinforced by the issuance of Permendesa No. 6 2020, which
gave the Village Budget (APBDesa) the power to prioritize
developments in the village.
Government policy support is certainly not capable of
increasing development in villages and reducing the disparity
between rural and urban areas. One of the reasons is there
remains a significant gap in allocating resources. The resource
gap in question includes human resources and infrastructure,
which are still very low in rural areas when compared to those
resources allocated to the city. This condition can be described
comprehensively through Indonesian Village Potential Data.
One of the methods that have been investigated by researchers
to reduce the disparity between villages and cities is by the
implementation of smart villages [1][2].
The concept of smart villages implemented in several
countries is based on the smart city development concept. This
concept certainly needs to be adjusted to the conditions and
potential factors present in these villages. Essential factors that
support the success of smart villages in various countries
include: resources, technology, infrastructure, and four-parties
synergy (academic, business, community, government). Four-
parties synergies are crucial to designing, building,
implementing, controlling, and maintaining the sustainability
of smart village programs [1][2][3]. The development for
implementing smart villages is increasing, along with the
rapid growth of information and communication technology
(ICT). ICT factor that is crucial to the success of smart villages
in several countries. ICT support is regulated by Permendesa
No. 6 of 2020, because it outlines the strategic areas, to
prioritise for rural development in Indonesia. Especially in the
era of data disruption, the readiness of ICTs systems provides
greater opportunities for the development of smart villages
based on big data analytics [4][5][6]. Research shows
implementing big data analytics has been successful in
climate-smart agriculture (CSA) based village programs
[7][8]. CSA programs that have been adopted in the smart
village models are mostly used for on-farm processes.
Research on the application of big data analytics in the smart
village for the post-harvest process and analysis of the
potential of the smart village has not done much. In contrast,
the factors of resources, infrastructure, and technology that are
presented in Indonesian villages can mostly be portrayed
through Village Potential Data published by BPS [9].
Implementation of big data, data analytic, data mining,
sensors, virtual reality, augmented reality, 5G technologies
are a need for future smart village research that is
collaborated as an ICT enrichment strategy [5][6]. This
strategy needs to be synergized with a policy framework
according to the conditions of local village wisdom.
Therefore, the main challenge for future smart village
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research is how to increase the ICT literacy of the villagers,
so that the smart village program is optimal. The strategies to
increase the ICT literacy of villagers is closely related to
citizen science [10][11]. The core of big data analytic that
will be implemented in the smart village program must
require at least three basic principles of big data (volume,
velocity dan variety) [12][13][14].
The big data-based smart village has also been
successfully carried out through the provision of electricity
and clean water, which is equipped with an IoT-based control
system [11][15][16]. This program is able to increase the
productivity of villagers. This productivity includes
agricultural, entrepreneurial, education, and health activities.
The implementation of smart transportation in the village area
has been done by using big data analytic concept [17]. Smart
transportation has collaborated with smart tourism, which is
claimed as the smart village innovation in Liaoning Province.
Implementation of a big data-based smart village must be
adjusted to the basic needs of the villagers. The development
of the big data-based smart village in Indonesia has a wide
variety of opportunities. This is influenced by the condition
of infrastructure, policies, and institutions of the economic,
social, cultural, and also political value in each village. These
conditions can be represented by secondary data through
Village Potential Data Indonesia.
Therefore, the main objective of this research is to analyze
the potential of implementing big data analytics-based smart
villages in Indonesia. The analysis of the smart village's
potential was carried out using the stages of Systematic
Literature Review (SLR) and integrated to multiple
regression. In this paper, we report an analysis of smart
villages based on big data analytic and the 2018 Village
Potential Data in Indonesia. Our smart village SLR's is
integrated with descriptive statistical analysis and clustering
of smart village research and citizen science. Using
descriptive multiple regression analysis, we report the
significant findings from the SLR about smart villages based
on 2018 Village Potential Data.
II. METHOD
This research was carried out according to the stages of
the SLR and multiple regression method, as shown in Fig. 1.
The SLR process was carried out with the NVivo 12 Plus
tools and this process also refer to [18]. The use of 44 papers
related to the smart village and 33 papers related to citizen
science also refer to [18]. We analyzed the hierarchy of
attributes and a hierarchy of the research domain [19] related
to the smart village and citizen science.
The research attributes hierarchy that is related to the
smart village and citizen science will produce dendogram
structure. This dendogram will map the density of smart
village research that is integrated into citizen science
research. The map constructed by hierarchical form included
year publication, Sustainable Development Goals (SDGs)
area, method and technology. This hierarchy will produce the
basic terminology of smart village integrated citizen science
as a foundation of the next model research. This basic
terminology will be used as reference to determine the
variable selected from the 2018 Village Potential Data of
Indonesia. SLR results are discussed with big data analytic
framework components, followed by further discussion,
about our analyses of 2018 Village Potential Data using
multiple regression techniques.
Fig. 1 Stages of research
The instrumentation of this research limited by using the
secondary data. The data used is the 2018 Village Potential
Data that is produced by BPS. This data consists of potential
variables in village development that exist around of the
administratively village area. Some of these variables are:
ICT, management of program, sources of funds for the
programs, agriculture and small-scale non-agricultural
businesses and beneficiary villagers. The selection of these
variable refers to [1][2][20]. The selected variable then
manage by multiple regression technique to map the potential
of big data analytic-based smart village in Indonesia.
III. RESULT AND DISCUSSION
A. Descriptive analysis of hierarchical attributes and
domains of smart village and citizen science research
The global condition of smart village research is
illustrated using an analysis of attribute hierarchies. We
conducted our analyses using NVivo 12 Plus tool, and the
results are shown in Fig 2. The analysis of a hierarchy among
the attributes of smart village research are arranged by year;
areas of Sustainable Development Goals (SDGs), followed
by research methods and technology. The process of our
research on the hierarchy in the smart village domain that
integrates with citizen science research is shown in Fig. 3.
The SLR result that represented in Fig 2 and Fig 3 refer to
[18], which is managing the paper of smart village and citizen
science research by descriptive analysis through hierarchical
attributes and domain research.
Based on our findings, it can be concluded that the smart
village research agenda in the future will have a potential to
be developed towards the construction, implementation and
comprehensive analysis of big data analytic technology. This
statement is supported by the availability of data and
technology that are becoming cheaper. Another support is
related to increasing access to various ICTs in rural areas,
demonstrated by an increase in ICT literacy in these areas.
One example of the uptake of ICT in Indonesia is the
increasing use of social media for sale and purchase
transactions in rural areas that is higher than in cities. The rate
of usage of social media to sell online in the village 69.9%
compared to the city, 62.3%. Using social media to buy
online in villages is 57.2%, while in cities 42.1% [21]. Even
internet usage by farmers in Indonesia has reached 10% of
total farmers in Indonesia [22].
This condition has a real potential opportunity for the
development of smart villages based on big data analytics.
Socialization through social media and the promotion of
smart villages has also become a research agenda for
progress, so that many parties can reproduce the success of
smart villages. There is research that proposes this will be
able to narrow urban and rural disparities, but with synergistic
and sustainable four-parties support [1][2][3][5][20][23]
[24][25][26].
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Fig. 2. Attributes Hierarchical Analysis of Smart Village Researches
Fig. 3. Attributes and Domain Hierarchical Analysis of Smart Village Researches
Fig 3 shows that the potential for developing smart village
research based on big data analytic is still very potential. The
results of the hierarchy analysis of the smart village research
domain and citizen science illustrate an area of research that
is still narrow when compared to other research areas. Smart
village research is still dominated by reviews related to social
science (citizen science & crowdsourcing) and infrastructure
(ICT and non-ICT facilities, and policies). There are
relatively less comments related to institutions in smart
village research, compared to other areas of research. We
propose, this shows that the development of smart village
research using big data analytics and integration with social
science, computer science and infrastructure; has significant
potential to be carried out especially in relation to institutional
collaboration.
B. Cluster analysis of smart village and citizen science
research
Smart village research cannot be separated from citizen
science research. Based on research finding [1][26] more than
70% of smart village research is comprehensively discussed
through the citizen science approach. Therefore, in this study,
cluster analysis is done through the investigation using the
word frequency technique of various text mining approaches
in citizen science research, and the integration of smart
village research with citizen science research. The results of
cluster analysis of citizen science research are shown in Fig
4. The results of cluster analysis of the integration of smart
village research in citizen science are shown in Fig 5.
Cluster analysis through word frequency techniques were
also carried out on 33 citizen science research papers to
produce results as five dominant words at the first level of the
dendrogram, as shown in Fig 4. The focus of citizen science
research is very closely related to smart village research.This
is evidenced by the dominant use of the words project and
data which are also dominant in the smart village research
dendogram. The word project belongs to the same family as
a citizen. In this case, it shows that the reported citizen project
activities [27][28]. The word data is closely related to
quality, its finding shows that citizen science research needs
high quality data. Especially, for the formation of citizens as
a scientist [29][30], this requires ICT and quality data
optimization techniques, to produce citizen science projects
that have high validity [29][31][32][33]. What is very
interesting about this word frequency technique is the
emergence of the farmer as a dominant word in the process
of citizen science research because it is implemented on
farmer residents, most of whom live in villages [34][35].
There are exciting findings from the results of our cluster
analysis using word frequency techniques in the integration
of smart village research and citizen science, with the results
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of the dendrogram structure, shown in Figure 5. Terminology
dominating the first level are the words smart and village.
Word frequency techniques have good potential and ability
for SLR, particularly in this application to smart village
research.
Fig. 4. The Dendrogram structure of citizen science's research
Fig. 5. Dendrogram structure of integration of smart village and citizen
science research
As described, this condition is strengthened by the
findings presented in the dendrogram of the integration of
smart village research and citizen science with a particular
focus on the terms : smart and village (first level); citizen and
projects (second level); and model (third level). This case
illustrates that most of the critical factors for the success of
smart villages are part of the building blocks of citizen
science. Other research reports, the development of citizen
science is also very responsive to the development of ICT
[8][36][37][38][39][40].
In Fig 5, at the sixth level is crowdsourcing, which is a
branch shared with the words community and model. This
interpreted to mean crowdsourcing an essential factor for the
success of smart villages implemented through a citizen
science project. The level of knowledge related to the role of
its citizens can be divided into four levels : 1) crowdsourcing,
citizens acting as sensors; 2) distributed intelligence, citizens
as interpreter bases; 3) participatory science, citizens
participating in the process of problem definition and data
collection, 4) extreme or collaborative science, citizens play
a role in the process of problem definition, data collection and
data analysis [41][42]. Therefore, those who will be involved
in a citizen science project can include professional scientists,
credentialed scientists, academic scientists, residents,
hobbyists, community members, volunteers, native villagers,
and human sensors [42][43][44].
C. Integration of smart village domain SLR and big data
management framework
Hierarchical analysis of the attributes and domains of
smart villages shows that the area of research related to big
data analytics being implemented and analyzed through the
integration of citizen science and infrastructure remains a
narrow field of research. This condition means there are
potential opportunities for development using this research
method. Moreover, it is strengthened by the results of a
descriptive analysis of the level ICT implemented. In
particular, the integration of big data analytics for the Opinion
Mining sub-sector, which is still minimal [5]. However, due
to the era of data disruption, data optimization using big data
analytic technology has been widely used in various fields
and is adopted in rural areas. The available data’s potential is
abundant, being accessible through social media, online
news, various government and private institutions' websites
and other on-line sources. The data in question are related to
agricultural commodity price, agricultural market potential
and other data that can be optimized using big data analytics.
The principle of big data management is built from a strong
data foundation and develops according to the procedure in
Fig 6. The big data management concept which is an
integrates concepts described in [4]. Entities in smart villages
that involve four-parties : 1) academics, 2) villagers
(community, cooperatives, micro small medium enterprises
(MSMEs), and farmers), 3) government (ranging from
villages level to nasional) and 4) businesses (entities from
upstream to downstream).
These entities can be optimized for developing smart
village big data management. An example of strong support
for the development of smart villages in Indonesia is Law
No. 6 of 2014. This legislates that villages can be used as a
basis for descriptive and predictive data to develop the
concept of big data analytics. The integration of the
conceptual framework indicated in Fig 6 with agriculture
management framework, that implemented in big data
analytics by [12] is shown in Fig 7. This case can be used to
identify and analyzed the components involved and influence
the implementation of the big data concept in smart villages.
The framework (Fig 7) shows that business processes are
focused on the use of big data in the management of
agricultural processes.
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Fig. 6. Conceptual of big data management
Fig. 7. The framework of a big data application system
There are three parts in business processes: 1) data chain,
2) agricultural management, and 3) agricultural processes.
Through various decisions, data chains interact with
agricultural processes and agricultural management
processes. The stakeholder network includes all stakeholders
involved with this process, not only significant data users but
other actors such as the community, MSME cooperatives, and
the government. Network management is the organizational
structure and technology of the network that facilitates
coordination and management of processes carried out by
stakeholder in the network management. The technology
component focuses on an infrastructure of information that
supports the data chain, while the organizational component
focuses on governance and business models.
Data chain refers to the sequence data capture decision
making and marketing of data [13][14]. The data chain
includes all activities needed to manage data for agricultural
management. Fig 6 illustrates the main steps in this chain that
are combined with the four components in the big data
framework. The big data analytic levels are descriptive,
diagnostic, predictive and prescriptive.
The ideal concept in the big data analytic framework is
very potential to be implemented in the smart village. This
smart village concept integrates components of social
sciences, computer science and infrastructure. The
establishment of the concept of smart villages based on big
data analytics can be reviewed more comprehensively
through a portrait of the potential of villages in Indonesia.
D. Descriptive analysis of Smart Villages Based on Big Data
Analytic Potencies in Indonesia
In this study, the potential of villages in Indonesia is
portrayed according to : readiness of their infrastructure
directly related to ICT; sources of funding for ICT activities
and the development of MSMEs; the involvement of villagers
in ICT management activities. The ICT infrastructure and
MSME potential studied in this study are limited to the results
from source data the 2018 Village Potential Data elaboration
in Indonesia [9].
Readiness to apply the concept of big data analytics for
smart villages can be mapped through the conditions of basic
needs, such as, the existence of a computer or laptop in the
village. The presence of computers in the village office has a
significant influence on the readiness of the village to become
a smart village. According to the availability of computers in
the village as an indication of readiness, there are only two
provinces with villages that are not equipped with computers
(80% of village offices have no computers). The provinces
are located in West Papua and Papua. More than 60% of
village offices in 32 other provinces have been equipped with
computers. This reflects on the outcome from Indonesia's
initial capital to develop smart villages; initial capital to
implement big data analytics [26]; and to focus strengthening
smart government [2][45][46].
Internet functionality for the development of big data
analytics has a vital role. The readiness of villages in
Indonesia shows very diverse conditions. Villages that have
more than 50% internet functionality are presence in the
provinces of Central Java, DI Yogyakarta, East Java and Bali.
This condition is following the accelerated development of
smart villages that have been initiated by DI Yogyakarta [2].
Villages in the provinces of West Java, Banten, West Nusa
Tenggara, Gorontalo and West Sumatra have internet
functionality ranging from 20-30%. Even though West Java
already has a Jabar Cyber Province (JCV) program that began
in 2013 and many ICT activities that collaboration in village
[47] . The conditions for village internet development are still
low. The conditions for internet functionality in West Java are
as follows: in 30% of functioning internet villages, 30% in
other villages internet conditions are rarely functioning, as
well as 30% of other villages the condition of the internet is
not functioning even the rest has no internet. Villages with no
internet available still dominate the provinces of West Papua
and Papua, and internet functionality in villages in other
provinces is still below 20%. This condition can be a
reference for mapping the development of smart villages
based on big data analytics [2] [5][48].
Internet functionality is certainly strongly supported by
the condition of internet signals that have entered the village.
Six provinces have 4G internet signals spread over more than
40% of their villages, namely the Bangka Belitung Islands,
West Java, Central Java, DI Yogyakarta, Banten and Bali.
The distribution of 3G internet signals with an average
distribution in more than 30% of the villages has covered 20
provinces in Indonesia. The authorized capital of 3G and 4G
signals can be optimized for the application of big data
analytics related to the development of smart villages in
Indonesia. Although currently, 5G technology has become a
smart village research agenda [5], the availability of 3G and
4G technology can still be optimized with four-partiet
integration [45][49].
The most critical ICT infrastructure related to the
availability of computers and the internet will not reach their
potential to develop smart villages based on big data
analytics, if not supported by useful ICT management.
Potential villages in Indonesia show that there are nine
provinces where the percentage of villages is more than 50%
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has been managing ICT. The provinces are West Sumatra,
Riau, Bangka Belitung Islands, West Java, Central Java, DI
Yogyakarta, East Java, Bali and West Nusa Tenggara. This
condition is critical because the contribution of ICT
management is an essential requirement in the development
of smart villages based on big data analytics [2][50][51][52].
The availability of infrastructure and management of ICTs
as the basis for implementing the concept of big data analytics
in smart villages in Indonesia also needs to be supported by
robust funding. Funding sources for ICT management in
villages have been strengthened by various schemes. The
2018 Village Potential Data in Indonesia confirms there are
18 provinces that implemented Swadaya funding schemes for
ICT management, in the range from 5-25%. This independent
funding source shows the principle of independence and very
high public awareness of the development of smart villages,
especially in the face of an era of data disruption and the
application of the concept of big data analytics [1][2]. This
condition can be used as a powerful initial capital and is
following the principle of smart village sustainability.
Provinces of Yogyakarta, Bali, Aceh, Central Java, Central
Sulawesi and West Java have implemented more than 10%
Swadaya schemes. The Yogyakarta Province shows the full
potential and readiness for the application of the concept of
smart villages based on big data analytics compared with
other provinces. In Yogyakarta, there has been a successful
implementation of smart villages in four village areas [2].
Community independence and government support
through funding are also vital assets for the sustainability of
big data analytic smart villages. Indonesia has the potential to
implement smart villages based on big data analytics, because
27 provinces have already implemented a mixed scheme of
Regional Budget Revenue (APBD) and Swadaya, in the range
25-80%. The sustainability of smart villages based on big data
analytics in Indonesia can be strengthened by the increasing
number of provinces implementing funding sourced from
Village Original Revenue (PAD), although the range 2-12%
is still low. The ten provinces : Jambi, Bengkulu, Bangka
Belitung Islands, Central Java, West Nusa Tenggara, East
Nusa Tenggara, West Kalimantan, Central Sulawesi, West
Sulawesi and Maluku manage village ICT through a PAD
scheme of more than 10%. This situation is interpreted as
powerful capital and effort from the village manager in order
to face the challenges in the era of data disruption. This
situation can be used as the foundation of guaranteeing the
sustainability of the smart village concept based on big data
analytics.
Government and community synergy are a very effective
strategy to ensure the sustainability of the concept of smart
villages based on big data analytics. All provinces in
Indonesia have adopted this strategy to strengthen
management of ICT in villages. This strategy is demonstrated
through the implementation (8-58%) of APBD funding
scheme and the mixed APBD-PAD scheme in all provinces.
This situation also occurs in the development of productive
agricultural businesses and non-agricultural small businesses.
All provinces in Indonesia have implemented this type of
development by relying on mixed sources of regional
Swadaya funds in the range 30-95%. When combined data
sources of ICT management funds and the development of
productive agricultural businesses and non-agricultural small
businesses, shows high relevance among provinces that apply
Swadaya funding sources to business development; who also
apply this scheme to ICT management in their villages. This
finding reinforces understanding that agriculture and non-
agricultural small businesses are supported by good ICT
management in the village [53]. This situation becomes very
important for the basic capital in the application of smart
villages based on big data analytics.
The involvement of villagers in ICT management
activities is a form of social capital that needs to be
strengthened. This potential possessed by Indonesians, is
confirmed by high rates of participation among citizens, poor
and farmers to contribute to various activities of ICT
management. Likewise, the beneficiaries of ICT management
have been absorbed by various elements of villagers,
including farmers and poor people spread across 22 provinces
with a percentage in the range 5-96%. This condition is in line
with the primary objective of the concept of smart villages
that are focused on reducing poverty levels in the village.
The potential application of a big data analytic smart
village is also assessed from the relationship between the
source of funds for managing agricultural businesses and non-
agricultural small businesses in the village and the program
beneficiaries. The study was conducted using multiple
regression analysis, and the results are shown in Table 1.
TABLE I. MULTIPLE REGRESSION ANALYSIS OF AGRICULTURE
& NON-AGRICULTURE SME'S MANAGEMENT AND FUND
SOURCES TO BENEFICIARIES IN THE VILLAGE*
APBD
PAD
Swadaya
Others
Poor citizen
Adjusted R Square
0.7040
Anova Signific. F
3.8800E-7
Anova P-value
0.0004
0.0106
0.0429
0.0692
Farmer / Fisherman
Adjusted R Square
0.9759
Anova Signific. F
7.7317E-24
Anova P-value
0.00002
0.0044
0.2291
0.5058
Community business group
Adjusted R Square
0.9482
Anova Signific. F
5.1609E-19
Anova P-value
0.0126
0.0024
0.0107
0.0023
Private / Businessman
Adjusted R Square
0.8567
Anova Signific. F
1.2138E-12
Anova P-value
0.4171
0.3142
0.0428
0.8539
*Source data processed from the 2018 Village Potential Data
Table 1 shows our results from the villages analysed in
financial support from various schemes, and opportunities of
development smart villages based on big data analytics. This
is indicated by the high value of Adjusted R Square. Even the
beneficiaries of the group of farmers/fishermen have a
powerful influence compared to other beneficiaries in
villages in Indonesia. This situation is relevant to the
objectives of the smart village program, which is more
focused on reducing poverty levels of farmers/fishermen
[7][53]. The effect of funding sources of this program has a
differing when examined in detail using ANOVA analysis.
Other sources of funds for the management of agricultural
businesses and rural non-agricultural small businesses have
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no significant effect on the poor. This condition is different
from the sources of APBD, PAD and Swadaya funds which
have a significant effect on the poor. This finding has
excellent potential for the development of smart villages
based on big data analytics because it demonstrates support
of the provincial and city/district governments is very high for
business development in the villages.
This scheme can be done through optimizing the sources
of APBD, PAD and Swadaya funds to reduce poverty for
villagers. Farmers/fishermen groups are significantly affected
by this program by the Regional Budget and PAD funds. In
contrast, the power of community Swadaya influences the
sustainability of smart village programs based on big data
analytics. All of the program's funding sources have an effect
on community business groups. The source of Swadaya only
influences the private sector/entrepreneurs and this is
interpreted here as meaning, the private sector/entrepreneurs
in the village have a right level of independence. The potential
application of smart villages based on big data analytics was
examined using multiple regression and ANOVA analysis of
ICT management and the variety of sources of funds for
beneficiaries from four groups of villagers (Table 2).
TABLE II. MULTIPLE REGRESSION ANALYSIS OF ICT
MANAGEMENT AND FUND SOURCES TO BENEFICIARIES IN THE
VILLAGE*
APBD
PAD
Swadaya
Others
Poor citizen
Adjusted R Square
0.5946
Anova Signific. F
3.2672E-06
Anova P-value
0.1037
0.6620
0.7965
0.6119
Farmer / Fisherman
Adjusted R Square
0.6006
Anova Signific. F
2.6581E-06
Anova P-value
0.0908
0.3623
0.7064
0.8509
Community business group
Adjusted R Square
0.9642
Anova Signific. F
2.50307E-21
Anova P-value
0.0006
0.5690
0.4064
0.5442
Most of citizen
Adjusted R Square
0.9965
Anova Signific. F
4.8495E-36
Anova P-value
0.0000
0,.000
0.4821
0.4407
*Source data processed from the 2018 Village Potential Data
Sources of funds for ICT management for beneficiaries of
the poor and farmers/fishermen are not closely related in this
program. In contrast, community business groups and citizens
in general who are strictly related to the ICT management
program and the source of funds used. This case shows that it
is necessary to strengthen the four-party collaboration in the
ICT management program in the village. With the program,
we can provide significant value to the effort to improve
living standards of the poor and farmers/fishermen. The
program will collaborate with various institutions including
academics, government and private sector (industry, e-
commerce, financial institutions, media) to provide various
forms of encouragement to villagers to become more
independent and take the initiative when developing smart
villages based on big data analytics.
Indonesia already has a strong enough potential to
develop a smart village based on big data, when it is related
to the development of telematics SMEs which are scattered in
a small part of rural areas [54] [55]. The development of SME
telematics is closely related to the industrial revolution
technology 4.0 [56]. This has led to the rapid development of
smart cities as a potential for strengthening the big data-based
smart village concept. The concept of smart village is also
increasingly sticking out along with various social, cultural,
economic and political changes due to the Covid-19
pandemic. Most of the community activities are carried out
through on-line facilities. Internet signal is a basic
requirement for many activities, especially educational
activities. The challenges of distance learning using multiple
e-learning platforms is becoming a very strong issue due to
the pandemic. This condition also occurs in rural areas, so it
becomes an opportunity to build smart education in a smart
village ecosystem based on big data analytic.
Education as one of the main goals in SDGs and smart
villages [6], is also an area of focus for strengthening the
Indonesian government during a pandemic. This is done to
ensure the continuity of education remains of high quality
even though it uses various online media. The adaptation
process continues to be carried out by the government and
even the community's role is getting stronger to support the
implementation of on-line based education [57]. This
condition is a very strong challenges because rural areas in
Indonesia that are connected to the internet are still relatively
low. This condition encourages the government to further
strengthen internet infrastructure through the Palapa Ring.
Palapa Ring will increase internet speed, especially in areas
that are difficult to reach. This effort is strengthened through
the collaboration of the Open Transportation Network
Program with the private sector.
This infrastructure is not only related to ICT
infrastructure, but also includes institutions and regulations
[58]. This condition provides better opportunities for creating
smart education, towards a smart community, smart people
and smart society [52]. Referring to [6], the indicators of the
success of smart education, especially those adapted for smart
village conditions, include three conversion factors : 1) smart,
2) sustain, 3) ICT. These three factors are related to six
indicators : 1) percentage of primary school availability, 2)
access to primary schools for a maximum of 30 minutes (if
walking), 3) number of e-book titles in the school library, 4)
number of computers, laptops, tablets and other interactive
digital media for learning media per class in basic schools, 5)
the number of computers, laptops, tablets and other
interactive digital media for learning media per class in
secondary schools, 6) the number of certified educational
activities in the fields of Science, Technology, Engineering
and Mathematics (STEM) per 10 thousand villagers. The
challenge of achieving smart education indicators is indeed
very strong if it is mapped to the villages potential conditions
in Indonesia. However, this is an opportunity for future
research to develop various concepts, models and
implementations of smart education in the big data-based
smart village ecosystem.
IV. CONCLUSION
Efforts to reduce poverty in rural areas in several countries
have been carried out through the implementation of smart
villages. The primary purpose of this study is to analyze the
potential of implementing smart villages based on big data
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analytics in Indonesia. This research contributes to the
analysis process that began with text mining-based SLR using
NVivo 12 Plus, and shows that the smart village research gap
based on big data analytic has potential to be studied. SLR
results through cluster analysis also show the dendrogram of
essential factors that can be elaborated on the formation of
smart village concepts based on big data analytics. The five
main factors are citizen, project, information, farmer and
model.
The results of the SLR integration of the smart village
research domain and the big data analytical management
framework show the collaboration of four-party entities are
the main focus of the process driver in the big village analytic-
based smart village concept. This four-parties optimization is
then strengthened by the quality of the data to be processed
into information, knowledge and wisdom using the four stages
of big data management : 1) description, 2) diagnosis, 3)
prediction and 4) prescription. For big data management to be
able to add value to the villagers, the four-party collaboration
needs to be the focus of the next study.
The results of the smart village research domain and the
big data management framework integrate with the
Indonesian Village Potential Data using multiple regression
techniques to review the potential application of big data
analytic smart villages in Indonesia. The agriculture and non-
agricultural small business management program in the
village supported by the APBD, PAD and Swadaya funding
schemes has had strong links with the empowerment of
farmers and community business groups with Adj values. R2
are 0.9759 and 0.9482, respectively. Likewise, for the poor,
the program is strongly correlated (Adj. R2 0.7040). This
finding can be an excellent social and economic capital for
the implementation of smart villages based on big data
analytics, which shows there has been a collaboration
between citizens, government and private parties in the
village for economic growth in the village through MSMEs.
This condition is different from the ICT management
program funded by the same scheme. Indonesia still needs a
strong capacity building through citizen science program
models that have been adopted by various countries, so that
ICT management programs have a more significant influence
on the poor (Adj. R2 = 0.5946) and poor farmers/fishermen
(Adj. R2 = 0,6006). The multiple regression results show that
the ICT management program with the support of APBD,
PAD, Swadaya and other funds has a powerful influence on
community business groups and citizens.
In individual provinces, Indonesia has excellent potential
for the application of smart villages based on big data
analytics. The system is strengthened by computer
infrastructure, the internet, financial support for business and
ICT management programs in the village, and social capital
in the form of active participation of villagers. Active
participation includes, both the poor, farmers/fishermen and
community business groups. Active participation is
strengthened by the existence of 27 provinces that have
implemented a mixed scheme of the Regional Budget
(APBD) and Self-subsistent (Swadaya), with a percentage
range between 25-80%. Sustainability of smart villages based
on big data analytics in Indonesia is strengthened by the
increasing number of provinces that are implementing
funding sourced from Village Original Revenue (PAD),
although the range 2-12% is still low.
The Swadaya source, which is collaborating with this
other scheme, shows the principle of independence and very
high public awareness for the development of smart villages,
especially in the face of an era of data disruption and the
application of the concept of big data analytics. There is an
exciting finding that reports provinces which apply
independent sources of funds in business development also
apply this scheme to ICT management in their villages. Our
analysis reinforces the interpretation that agriculture and non-
agricultural small businesses are indeed supported by proper
ICT management in the village. This situation is very
important for social, economic and cultural capital applied to
the development of smart villages based on big data analytics.
This concept has the potential to be implemented,
especially with the social, economic, cultural and political
changes due to the Covid-19 pandemic. The concept of a
smart village based on big data analytic can be a new force to
build a smart community, through smart education, towards a
smart society in a smart village ecosystem based on big data
analytics. The strength of social, economic, political and
cultural capital in smart village infrastructure based on big
data is an important factor for the successful collaboration of
infrastructure facilities (both ICT and non-ICT), institutions
and regulations. Therefore, the challenges for future research
is to develop smart village models based on big data analytics
that are suitable for Indonesia. This needs to be elaborated
with the diversity of economic, social and cultural potential
and capital as well as the potential of data technology
available on various media, to strengthen the economic, social
and cultural village through the framework of agriculture big
data analytic.
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