Pengaruh Metode Word Embedding dalam Vector Space Model pada Pemerolehan Informasi Materi IPA Siswa SMP - Ibnu Rasyid Wijayanto; Imam Cholissodin; Yuita Arum Sari (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
The Covid-19 pandemic in early 2019 made the face-to-face learning system in schools transformed into online learning. Online learning requires students to access digital learning materials, but the materials in the search results is often too broad which causes difficulties for students including junior high school students. This can be overcome with an Information Retrieval System that can make it easier for junior high school students to learn the desired materials, for example science materials. The Information Retrieval System in this study uses the Vector Space Model (VSM) method and the weighting using the Term Frequency Inverse-Document Frequency (TF-IDF) method. Systems that use the TF-IDF and VSM methods are tested with a combination of the TF-IDF, VSM and Word Embedding methods to determine the effect of the Word Embedding Method on the system. The result from this research is that word embedding can have an effect. The precision, recall, F-measure and accuracy values in the combined system test of the VSM and TF-IDF methods are 0.395, 0.8628, 0.5375, and 0.9306, respectively. The precision, recall, F-measure and system test accuracy values with the addition of Word Embedding in the VSM and TF-IDF methods are 0.38, 0.8880, 0.52822, and 0.9286, respectively. The effect of Word Embedding is that word embedding retrives more documents so that the range of documents obtained is larger. However, the use of additional word embedding in the vector space model can cause a reduction in the level of relevance because documents that should be irrelevant and unwanted by the user are likely to be retrieved by the system.