Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm
The construction of smart villages has begun in many Indonesian villages, along with the advancement of technology and local economic growth. Villagers must participate in constructing the smart economy-smart village by becoming familiar with the characteristics of the village's inhabi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2023-01-01
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| Series: | Decision Science Letters |
| Online Access: | http://www.growingscience.com/dsl/Vol12/dsl_2023_19.pdf |
| _version_ | 1797812606346461184 |
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| author | Eneng Tita Tosida Riko Setiawan Irma Anggraeni Roni Jayawinangun Sukono Sukono Jumadil Saputra |
| author_facet | Eneng Tita Tosida Riko Setiawan Irma Anggraeni Roni Jayawinangun Sukono Sukono Jumadil Saputra |
| author_sort | Eneng Tita Tosida |
| collection | DOAJ |
| description | The construction of smart villages has begun in many Indonesian villages, along with the advancement of technology and local economic growth. Villagers must participate in constructing the smart economy-smart village by becoming familiar with the characteristics of the village's inhabitants using the citizen science model. This study intends to categorize villagers so that researchers can assess and decide their level of readiness for a smart economy in an ecosystem based on a smart village. Clustering is required to find communities of residents who are ready based on their traits. Using fuzzy C-Means with a Davied Bouldin Index value of 0.129, the data were divided into 4 clusters. The most important variables were chosen using information from the test's 300 responders, and the Kaiser Mayer Olkin assumption of 0.975 was used to validate the results. Our paper provides new information on how smart village readiness is assessed by the citizen science cluster. It was decided to divide residents into four groups: those who are less prepared (24.33%), those who are somewhat prepared (29.33%), those who are ready ( 25.67%) %), those who are ready (level of participatory knowledge), and those who are very ready for the smart economy (20.67%) based on the cluster model. |
| first_indexed | 2024-03-13T07:39:59Z |
| format | Article |
| id | doaj.art-be7b90e08e2a4594863970ab6dd15e58 |
| institution | Directory Open Access Journal |
| issn | 1929-5804 1929-5812 |
| language | English |
| last_indexed | 2024-03-13T07:39:59Z |
| publishDate | 2023-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | Decision Science Letters |
| spelling | doaj.art-be7b90e08e2a4594863970ab6dd15e582023-06-03T16:46:03ZengGrowing ScienceDecision Science Letters1929-58041929-58122023-01-0112361762810.5267/j.dsl.2023.4.003Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithmEneng Tita TosidaRiko SetiawanIrma AnggraeniRoni JayawinangunSukono SukonoJumadil Saputra The construction of smart villages has begun in many Indonesian villages, along with the advancement of technology and local economic growth. Villagers must participate in constructing the smart economy-smart village by becoming familiar with the characteristics of the village's inhabitants using the citizen science model. This study intends to categorize villagers so that researchers can assess and decide their level of readiness for a smart economy in an ecosystem based on a smart village. Clustering is required to find communities of residents who are ready based on their traits. Using fuzzy C-Means with a Davied Bouldin Index value of 0.129, the data were divided into 4 clusters. The most important variables were chosen using information from the test's 300 responders, and the Kaiser Mayer Olkin assumption of 0.975 was used to validate the results. Our paper provides new information on how smart village readiness is assessed by the citizen science cluster. It was decided to divide residents into four groups: those who are less prepared (24.33%), those who are somewhat prepared (29.33%), those who are ready ( 25.67%) %), those who are ready (level of participatory knowledge), and those who are very ready for the smart economy (20.67%) based on the cluster model.http://www.growingscience.com/dsl/Vol12/dsl_2023_19.pdf |
| spellingShingle | Eneng Tita Tosida Riko Setiawan Irma Anggraeni Roni Jayawinangun Sukono Sukono Jumadil Saputra Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm Decision Science Letters |
| title | Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm |
| title_full | Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm |
| title_fullStr | Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm |
| title_full_unstemmed | Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm |
| title_short | Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm |
| title_sort | modeling of citizen science cluster in making decision for readiness towards bogor smart village an application of fuzzy c means algorithm |
| url | http://www.growingscience.com/dsl/Vol12/dsl_2023_19.pdf |
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