印尼建设大数据智慧村的公民科学前景聚类

E. T. Tosida, Suprehatin Suprehatin, Y. Herdiyeni, Marimin Marimin, Indra Permana Solihin
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引用次数: 7

摘要

发展公民科学是智慧村建设的基础,是解决农村贫困问题的途径之一。本研究的主要目的是绘制印尼构建基于大数据的智慧村的公民科学前景集群。本研究通过对印度尼西亚2018年村庄潜力数据进行聚类分析,结合使用k-means、期望最大值和基于密度的算法。本研究的贡献是绘制了印度尼西亚33个省的智慧村公民科学集群的前景图。使用的数据仅限于村庄行政区域,因此雅加达DKI未纳入本研究。该聚类分析的主要因素为ICT基础设施、村民参与ICT活动的管理、可再生能源、交通运输、农业经营活动和非农业中小企业。这些集群的结果表明,发展智慧印尼村庄的公民科学潜力有3个集群,即非常有潜力(11%)、潜力(60%)和相当有潜力(29%)集群。这张公民科学前景聚类图是基于印度尼西亚各省的空间数据可视化的。邦加勿里洞岛、西爪哇、中爪哇、东爪哇、日惹、万丹和巴厘岛是发展公民科学以建设基于大数据的智慧村庄的潜在省份。用系统文献综述(SLR)的树突图结构对聚类结果进行了验证。树形图结构显示了与聚类过程中使用的主要属性相对应的关键字。这个验证过程是智能村庄研究生态系统中的一个创新发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering of Citizen Science Prospect to Construct Big Data-based Smart Village in Indonesia
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 nonagricultural 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.
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