Analisis Topik Penelitian Pendidikan Matematika Di Indonesia Dengan Menggunakan Metode Latent Dirichlet Allocation (LDA)
Keywords:
Topic Modeling, Latent Dirichlet Allocation (LDA), Scientific publication abstracts, Mathematics EducationAbstract
On the research topic of Mathematics Education readers or researchers still have difficulty identifying research topics in the field of Mathematics Education. This is because there is no system or model that can be seen or used in determining research topics. Besides that, there is no automation of the research direction of Mathematics Education in Indonesia using topic modeling, so it is necessary to conduct a study or research on this. In research, the most important thing is the trend of research that is currently developing so that it can determine the novelty of the studies that have been done before. While there is no system used to determine trends and state of the art from research in the field of Mathematics Education. The aim of the research is to find out an overview of the research topics in Mathematics Education in Indonesia in 2020-2023 and to find out the implementation of modeling research topics in Mathematics Education in Indonesia using the Latent Dirichlet Allocation (LDA) method for 2020-2023. The research design consisted of literature study, data collection, data pre-processing: tokenization, case folding, stopword removal, and stemming, topic analysis with LDA, evaluation of the LDA method, and conclusions. Analysis of Topic Modeling with Latent Dirichlet Allocation using packages used from python including the Gensim and pyLDAvis packages. Based on the coherence score, the best number of topics (K) = 18, with a coherence score = 0.426 (the highest), it can be concluded that the number of topics produced is 18 topics.
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