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首页> 外文期刊>Jurnal RESTI: Rekayasa Sistem dan Teknologi Informasi >Analysis of Health Research Topics in Indonesia Using the LDA (Latent Dirichlet Allocation) Topic Modeling Method
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Analysis of Health Research Topics in Indonesia Using the LDA (Latent Dirichlet Allocation) Topic Modeling Method

机译：使用LDA（潜在Dirichlet分配）主题建模方法分析印度尼西亚的健康研究主题

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摘要

In this time, the need of research, the development and the implementation of the result of research in health is increasing both from the researchers, the government, the academic even of from the public general. One of the ways to find out the health research trend is by topic modeling. The method that used in this research is topic modeling LDA (Latent Dirichlet Allocation) method. The purpose of this research is to identify how modeling topic method LDA analyze modeling topic to some health research in Indonesia by Sinta Journal and to know how the coherence value in each topic of the model that has been made. Besides, hopefully it can be used as a reference to do heath research in Indonesia based the topic that has been modeled. The development of this research uses Anaconda3 Python Programming Language Tools and utilizes the LDA library that provided to get the topic model. To examine the result of this research the respondent are medical worker, health researcher and academics. The result of this research the topic  modeling that used 94,1% respondent say very good and 5,9% say good.
机译：在这段时间内，研究人员，政府，政府的研究结果的需求，发展和实施的需要越来越大。找出健康研究趋势的方法之一是主题建模。本研究中使用的方法是主题建模LDA（潜在Dirichlet分配）方法。本研究的目的是确定模型主题方法如何通过Sinta Journal将建模题为LDA分析到印度尼西亚的某些健康研究中的建模主题，并了解所做的模型的每个主题中的一致性值。此外，希望它可以作为参考印度尼西亚的Heath研究基于已建模的主题。该研究的开发使用Anaconda3 Python编程语言工具，并利用提供的LDA库来获取主题模型。检查这项研究结果，受访者是医务人员，卫生研究员和学者。这项研究的结果是使用94,1％受访者表示非常好的主题建模和5,9％的人说好。

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