集合岩在廖内省基础设施Slb分类中的应用

Munzhiroh Rizki Minallah
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引用次数: 0

摘要

2020年的入学人数表明,20.56%的残疾儿童没有或从未上过学(BPS, 2020年)。这表明仍有许多残疾儿童没有得到充分的教育。因此,需要增加对残疾儿童设施的提供和受教育机会的关注,以使残疾儿童和非残疾儿童之间在上学方面不存在不平等。对特殊学校数据的统计方法可以应用于各种目的。对混合类型数据进行分组的方法是集成。在本研究中,在廖内省的47所特殊学校中使用了集成ROCK(鲁棒聚类使用链接)方法。在ROCK集合方法中,使用0.22的值,我们得到了3个最优聚类,其比值为0.08177794。调查发现,与其他集群相比,集群3没有足够的设施,如实验室、图书馆和互联网网络。可以说,集群3比其他集群更需要关注。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensemble Rock Application for Classification of Slb in Riau Province Based on Infrastructure Facilities
The 2020 school participation figure states that 20.56% of children in the disability category have the status of not/never been to school (BPS, 2020). This shows that there are still many children with disabilities who have not received adequate education. Therefore, attention to the availability of facilities and access to education for children with disabilities needs to be increased so that there is no inequality of school participation between children with disabilities and non-disabled children. On extraordinary school data statistical methods can be applied for various purposes. The method that can be used to group mixed-type data is ensemble. In this study, the ensemble ROCK (Robust Clustering using links) method was used at 47 extraordinary schools in Riau Province. Using the value 𝜃 of 0.22 in the ROCK ensemble method, we get 3 optimal clusters with a ratio of 0.08177794. It was found that cluster 3 is a cluster that does not have adequate facilities such as a laboratory, library and internet network than other clusters. It can be said that cluster 3 needs more attention than other clusters.
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