Clustering of Citizen Science Prospect to Construct Big Data-based Smart Village in Indonesia
Eneng Tita Tosida Computer Science Dept.
Pakuan Univeristy Bogor, Indonesia [email protected]
Suprehatin Suprehatin Agrobusiness Dept.
IPB Univeristy Bogor, Indonesia [email protected]
Yeni Herdiyeni Computer Science Dept.
IPB Univeristy Bogor, Indonesia [email protected]
Marimin
Industrial Technology Dept.
IPB Univeristy Bogor, Indonesia [email protected]
Indra Permana Solihin Informatic Dept.
UPN Veteran Jakarta, Indonesia [email protected]
Abstract— The development of citizen science as the foundation of a smart village is one of the solutions to reduce poverty in rural areas. The main objective of this research is to map the prospect cluster of citizen science to construct big data- based smart villages in Indonesia. This research was conducted through a cluster analysis of 2018 village potential data in Indonesia, using a combination of k-means, expected maximum and density-based algorithms. The contribution of this research resulted in a map of prospect of citizen science clusters for smart villages in 33 provinces in Indonesia. The data used was limited to village administrative areas, so DKI Jakarta was not included in this study. The main factors that attribute to this cluster analysis are ICT infrastructure, management of villagers' participation in ICT activities, renewable energy, transportation, agricultural business activities and non- agricultural small and medium enterprises. The results of the clusters show that there are 3 clusters of citizen science potential to develop smart Indonesian villages, namely the very potential (11%), potential (60%) and quite potential (29%) clusters. This citizen science prospects cluster map is visualized on spatial data based on the provinces in Indonesia. Province Bangka Belitung Island, West Java, Central Java, East Java, Yogyakarta, Banten and Bali are potential provinces for developing citizen science in order to construct big data-based smart villages. The results of this cluster were validated with the dendogram structure of the systematic literature review (SLR). The dendogram structure shows the keywords that correspond to the main attributes used in the clustering process. This validation process is an innovative finding in the smart village research ecosystem.
Keywords—Agriculture, Big Data, Clustering, Citizen science, Smart village
I. INTRODUCTION
The poverty rate in rural Indonesia is still relatively higher when compared to urban areas (13.3% in rural areas and 7.26% in urban areas [1]). The young generation considered that job opportunities in agriculture are not prospective. The data show that the composition of Indonesian farmers by age is shown in Figure 1 [1]. Farmers in Indonesia are still dominated by people over 45 years of age with an accumulated percentage of up to 82%. Only 2% of farmers are less than 35 years old. The younger generation, who is often referred to as the millennial generation, is more interested in technology-based jobs. Meanwhile, agriculture in Indonesia is still dominated by conventional methods.
Fig. 1. Percentage of age of Indonesian farmer
Efforts to reduce the poverty level of farmers in rural areas have also become a priority program in several countries. One concept that has succeeded in reducing the poverty level of farmers in rural areas is the implementation of the smart village concept[2][3][4]. The key to the success of the smart village program is the participation of citizens and stakeholders who work together to achieve the goals of a smart village[5][6][7]. The key to the success of citizen science is the setting of clear objectives, data reliability, citizen empowerment, optimal communication, contribution to science, and can be a synergistic reference for research development[8][9]. The proposed smart village concept is also expected to be a driving force to increase the interest of the millennial generation to contribute to innovation and technology-based agriculture. The concept of a smart village as a solution to reducing poverty in rural Indonesia is very prospective to be implemented. Initial research has produced the basic concept of smart villages in Indonesia [10][11][12].
The ICT model can even improve the performance of village governments to realize the embryo of smart governance [13][14][15][16][17]. In fact, as has been explained by [2][3][18] the key to the success of a smart village is through optimization of citizen science. The potential for developing citizen science as the foundation for a smart village can be assessed from village potential data.
Indonesia already has data on the potential of each village which is displayed in detail [19]. This data potentially to be integrated with data from other sources that can fulfill the 5V's concept of big data [20]. This data is important to map because
1% 1%
26%
31%
25%
16%
<25 25-34 35-44 45-54 55-64 ≥65
it is compatible with big data management architecture [21].
Integration can also be done with official data released by the government and data acquired through supervisory agencies in the village environment. The availability of this data is the biggest challenge in this study. Therefore, it is necessary to conduct initial research to formulate the potential of citizen science in the concept of a smart village based on big data in Indonesia. The concept of 5V's of big data needs to be supported by the criteria for volume, variety, velocity, veracity and value. Efforts to meet these criteria can be done by conducting preliminary research related to mapping the potential for citizen science in the village, by utilizing village potential data.
The main objective of this research is to build a citizen science prospect cluster model as the foundation for a smart village based on big data in Indonesia through a data mining approach using clustering learning techniques. The variables involved refer to the main village resources needed to build a smart village [6]. The variables involved also refer to Permendesa No. 6 2020 which powers the Village Budget (APBDesa) for priority developments in the village.
II. METHOD
This research was conducted through the stages of the method as shown in Figure 2. The initial step of the research was to conduct a descriptive analysis of the potential data for Indonesian villages in 2018. The k-means algorithm is varied by means of distance determination techniques and its initialization into 6 variations: 1) euclead-random, 2) euclead-k-means, 3) euclead-canopy, 4) manhattan-random, 5) manhattan-k-means, 6) manhattan-canopy. Clustering was carried out using Weka 3.8.4 tools and visualization of cluster results was carried out through a map coloring process using the https: paintmaps.com/map-charts/97/Indonesia-map- chart [22] facility. Validation of cluster results was carried out through qualitative testing of keywords in the SLR dendogram. The SLR includes the integration of citizen science and smart village research.
Since the main objective of this research is to model the potential of citizen science for smart villages in Indonesia, the cluster analysis is carried out in 2 stages. The first stage is clustering of 62847 village data in Indonesia. The second stage is the clustering aggregation of citizen science potential to build smart villages at the provincial level. The results of the cluster analysis were elaborated into potential data for Indonesian villages, and visualized on a map of Indonesia based on 33 provinces. The government system at the village level in Indonesia is divided into 2, namely the village government in the district government and the sub-district government in the city government area. In this research, the data were selected and focused on village administrations in the district area. DKI Jakarta Province was not involved because all of its administrative areas are cities. The variables used in the cluster analysis were 25 variables. These variables include 9 variables related to ICT infrastructure, 4 variables related to transportation management, 4 variables related to renewable energy management, 4 variables related to ICT management, and 4 variables related to transportation management.
Start
Variable determine of citizen science related smart village from Potential Village Data
2018
Descriptive statistic analysis
Clustering analysis clustering of citizen science prospect in big
data based smart village : using K-Means,
EM, Density Based by Weka 3.8.4 Validation cluster
analysis according to citizen science, infrastructure and component of big data based smart village concept Finish
Fig 2. Stages of research
III. RESULT AND DISCUSSION
A. Determining variabel of potential citizen science to construct smart village in Indonesia
The determination of the variables to be used in the mapping of potential citizen science clusters refers to [8][9].
The fund variable was chosen because the main capital for village development is the source of funds. In accordance with Law No. 6 2014 concerning Villages and Village Minister Regulation No. 6 2020 concerning priority use of village funds, it has spurred development and development in villages, including sector development (ICT). This is influenced by policies and community activities as well as socio-cultural and economic factors in the village community [23].
B. Descriptive analysis of potential citizen science to contruct smart village in Indonesia
Descriptive analysis of the potential of citizen science to build a smart village begins with a description of the source of village funds. The source of village funds is represented in 4 main activities, namely the management of ICT, agricultural business and small and medium enterprises in the village, transportation and renewable energy. The representation of the source of these funds is shown on the radar diagram shown in Figures 3 to 6. The analysis of the description of the potential of citizen science for this smart village also refers to the Sustainable Development Goals (SDGs) [24] and the potential of artificial intelligence SDGs [25]. The data in Figures 3 to 6 shows the percentage of villages that carry out these main activities and are funded by 4 types of funding. Villages in provinces on the island of Java generally have the power of social capital to build a citizen science ecosystem in order to create a smart village. There are interesting findings from Figures 3 to 6 that it turns out that other regions also show very good prospects for building big data-based smart villages.
Figure 3 shows that the power of Village Original Income (PADesa) in the provinces of Central Java, Yogyakarta and East Java has the potential to be used to build ICT-based citizen science. Village development is also supported by the strength of the Swadaya fund and other funds. Swadaya is the power of funds that comes from the independence of the village community [6][26]. There are interesting findings that show that villages in North Maluku are strengthening funding for ICT management programs through other funding schemes. This is a very good potential for the development of citizen science ecosystem in order to build big data-based smart village [9][27].
Fig. 3. Source of funding of ICT management program in the village
Fig. 4. Source of funding of agriculture business and SMEs business in the village
Fig. 5. The source of funds for the renewable energy program in the village
Fig 6. The source of funds for the transportation management program in the village 0.00
0.20 0.40 0.60 0.80 1.00
ACEH NORTH SUMATERA WEST SUMATERA RIAU JAMBI SOUTH SUMATERA BENGKULU LAMPUNG KEPULAUAN BANGKA… KEPULAUAN RIAU WEST JAVA CENTER JAVA DI YOGYAKARTA EAST JAVA BANTEN BALI WEST SOUTHEAST NUSA EAST SOUTHEAST NUSA WEST KALIMANTAN CENTER KALIMANTAN SOUTH KALIMANTAN EAST KALIMANTAN NORTH KALIMANTAN NORTH SULAWESI CENER SULAWESI SOUTH SULAWESI SOUTHEAST SULAWESI GORONTALO WEST SULAWESI MALUKU NORTH MALUKU WEST PAPUA PAPUA
APBD PAD SWADAYA OTHERS
0.00 0.20 0.40 0.60 0.80 1.00
ACEH NORTH SUMATERA WEST SUMATERA RIAU JAMBI SOUTH SUMATERA BENGKULU LAMPUNG KEPULAUAN BANGKA… KEPULAUAN RIAU WEST JAVA CENTER JAVA DI YOGYAKARTA EAST JAVA BANTEN BALI WEST SOUTHEAST NUSA EAST SOUTHEAST NUSA WEST KALIMANTAN CENTER KALIMANTAN SOUTH KALIMANTAN EAST KALIMANTAN NORTH KALIMANTAN NORTH SULAWESI CENER SULAWESI SOUTH SULAWESI SOUTHEAST SULAWESI GORONTALO WEST SULAWESI MALUKU NORTH MALUKU WEST PAPUA PAPUA
APBD PAD SWADAYA OTHERS
0.00 0.20 0.40 0.60 0.80 1.00 1.20
ACEH NORTH SUMATERA WEST SUMATERA RIAU JAMBI SOUTH SUMATERA BENGKULU LAMPUNG KEPULAUAN BANGKA BELITUNG KEPULAUAN RIAU WEST JAVA CENTER JAVA DI YOGYAKARTA EAST JAVA BANTEN BALI WEST SOUTHEAST NUSA EAST SOUTHEAST NUSA WEST KALIMANTAN CENTER KALIMANTAN SOUTH KALIMANTAN EAST KALIMANTAN NORTH KALIMANTAN NORTH SULAWESI CENER SULAWESI SOUTH SULAWESI SOUTHEAST SULAWESI GORONTALO WEST SULAWESI MALUKU NORTH MALUKU WEST PAPUA PAPUA
APBD PAD SWADAYA OTHERS
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
ACEH NORTH SUMATERA WEST SUMATERA RIAU JAMBI SOUTH SUMATERA BENGKULU LAMPUNG KEPULAUAN BANGKA BELITUNG KEPULAUAN RIAU WEST JAVA CENTER JAVA DI YOGYAKARTA EAST JAVA BANTEN BALI WEST SOUTHEAST NUSA EAST SOUTHEAST NUSA WEST KALIMANTAN CENTER KALIMANTAN SOUTH KALIMANTAN EAST KALIMANTAN NORTH KALIMANTAN NORTH SULAWESI CENER SULAWESI SOUTH SULAWESI SOUTHEAST SULAWESI GORONTALO WEST SULAWESI MALUKU NORTH MALUKU WEST PAPUA PAPUA
APBD PAD SWADAYA OTHERS
Prospects of citizen science can also be studied from the agricultural business activities and non-agricultural small and medium enterprises funded in the village. This condition shows that village independence is the initial capital for the development of a citizen science ecosystem in the context of smart village development [28].
C. Cluster analysis of citizen science prospect in big data- based smart village
Smart village research cannot be separated from citizen science research. Based on research [3][28] shows that more than 70% of smart village research is discussed comprehensively through the citizen science approach.
Therefore, in this study, a cluster analysis was carried out using 6 variations of the k-means algorithm, as well as the expected maximum and density based. The clustering process was carried out on the 2018 Indonesian Village Potential Data, involving 62,847 data. The attributes used include ICT infrastructure, ICT management activities, transportation, renewable energy, and agricultural business in the village. The total attributes used in the citizen science prospect cluster analysis to build a big data-based smart village in Indonesia are 25 attributes (shown in Table 1).
TABELI.ATTRIBUTES OF CLUSTER ANALYSIS
No. Atribut No. Atribut
1. Cable phone 14. Renewable energy
management
2. Mobile phone 15. Fund sources of ren.
energy management 3. Interne Cafes (Warnet) 16. Implementer of ren.
energy management
4. BTS 17. Benefaciaries of ren.
energy management
5. Provider 18. ICT management
6. Mobile phones signal 19. Fund sources of ICT management
7. Internet signal 20. Implementer of ICT management
8. Computer / Laptop 21. Benefaciaries of ICT management
9. Internet functionality 22. Agriculture business management
10. Trasnportation management
23. Fund sources of agriculture business management
11. Fund sources of transportation
managemetn
24. Implementer of agriculture business management
12. Implementer of transportation
management
25. Benefaciaries of agriculture business management
13. Benefaciaries of transport.
Management
The prospect cluster process for citizen science to build a big data-based smart village in Indonesia is then aggregated at the provincial level. The cluster results for the cluster technique combination are shown in Appendix 1. The results of the selected clusters are shown in Figure 6. The cluster results are also mapped on a map of provinces in Indonesia as shown in Figure 7.
Based on the results of clustering, it shows that 8 combinations of cluster techniques produce almost the same pattern. The prospects for citizen science to build a big data-based smart village in Indonesia are divided into three clusters, namely, very potential, potential and quite
potential. The 25 attributes used show a very significant effect on cluster formation. There is a significant difference between the results of the descriptive analysis of the four activities as shown in Figures 3 to 6 with the results of the clustering.
Fig 6. Cluster result of citizen science prospect to construct big data- based smart village in Indonesia
Fig. 7. Cluster map of of big data-based smart village potencies in Indonesia
The clustering process shows more precise results to see the potential level of citizen science in a big data-based smart village environment. Citizen science ecosystem needs to be built on the initiation of villagers supported by stakeholders in the four-party concept [3][6][26]. The dimensions of a smart village owned by Yogyakarta to date are a smart village based on smart economy (Kulonprogo Regency), smart tourism (Pulepayung), smart governance (Dlingo Village) and smart living (Patehan residents).
Equally with West Java and Kep. Bangka Belitung Islands which already has 2 smart villages based on smart tourism and smart living [6][15].
D. Validation cluster analysis according to citizen science, infrastructure and component of big data-based smart village concept
The results of clustering were validated through the opinions of researchers in the field of citizen science and smart villages elaborated through the Systematic Literature Review (SLR). This SLR is carried out using a text mining approach, with word frequent techniques on 33 citizen science papers and 44 smart village papers. The SLR results of citizen science prospects in big data-based smart village ecosystems are displayed in a dendogram structure [26].
This dendogram contains keywords that can be used as a reference for the validation process of citizen science prospect clustering. The dendogram structure resulting from the integration of SLR with smart village paper and citizen science shows that the main keywords are smart and village in the main branch. Keywords in the second branch are citizen and project, and in the third branch are
information and farmers and models. These keywords indicate conformity with the attributes used in this clustering process. This validation method can be used as one of the most innovative findings. The strength of the text mining process using NVivo tools is able to extract keywords that can be used as a reference as a factor in developing citizen science in big data-based smart village environments.
Fig. 8. Dendogram of big data-based smart village concept
The keyword citizen is closely related to the community in the village which is the initiator and implementer of the program related to the citizen science project. Community strength which is supported by similarity in ethnicity, regional origin, common vision and goals is a factor in the success of village development programs [29]. The synergy four-partite concept will expand the reach of the success of this citizen science program [29]. Village communities that are still at a very strong level of solidarity, live in cooperation, maintain ethics and norms, as well as upheld local wisdom, are expected to synergize with the power of ICT which is able to become a media of socialization, even a technology bridge to succeed smart village programs [27]. Youth communities will be more adaptive to ICT developments [5] [9].
This research is the initial foundation for mapping the potential of big-data based smart villages in Indonesia. The cluster results can be enriched with data integration which varies according to the character of 5V's big data [20].
These decisions can be made through diagnostic, predictive and prescriptive models and integrated with smart village entities [26][28] in the framework of big-data application systems [21].
IV. CONCLUSION
The prospects for the citizen science program as a foundation for the development of big data-based smart villages in Indonesia can be studied creatively through a clustering approach.
The six clustering models show the same pattern, so that they are able to map three prospect clusters of citizen science. The potential cluster covers the province of Bangka Belitung Island, West Java, Central Java, East Java, Banten and Bali (11%). The potential cluster covers 60%
of the province and quite potential covers 29%. The cluster model validation was carried out in an innovative way, namely by looking for suitability in the dendogram structure of the SLR results. The SLR result is an integration of citizen science research with smart villages.
The main keywords that are in accordance with the conditions of cluster forming attributes are the words citizen and project, information and farmers. This keyword can be used as a key factor to increase the potential that exists in the village in the province which is quite potential.
The strength of the community and the synergistic support of the private sector (business), government and academics can further enhance the prospects for citizen science for the success and sustainability of big data-based smart villages in Indonesia. The next research opportunity is to integrate village potential data with data sourced from various media, such as social media, news portals, spatial- based village infrastructure data and data from official government portals, so as to be able to produce artificial intelligence-based decisions within the big-data application system framework. adjusted to the entity level in the smart village ecosystem.
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