WCSE

Prediction Questions for National Examination with Generalized Vector Space Model (GVSM) Algorithm

Abstract— National Examination (UAN or UNAS or UN) is a government evaluation measurement tool used to determine the quality of education in Indonesia. Quality is evidenced when students can answer questions in the national examination based on materials aligned with the Graduate Competency Standards (SKL). Materials published in the past can also be republished for the next exam. This SKL is divided into several research topics that are relevant to current trends. This study aims to determine how effective the use of the Generalized Vector Space Model (GVSM) algorithm is in predicting each question that will appear. The GVSM algorithm determines the similarity between words that appear in one document and words that appear in another. The GVSM algorithm is used to determine the similarity between words that appear in one document and words that appear in another document. The evaluation results showed an accuracy of 0.75, a precision of 0.7321, and a recall of 0.7017.

Index Terms— national examination, GVSM, prediction question, similarity word, evaluation

Nurma Ayu Wigati
Faculty of Mathematics and Natural Science, Universitas Indonesia, INDONESIA
Ajeng Dwi Asti
Faculty of Information Technology, Universitas Andalas,INDONESIA

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Cite: Nurma Ayu Wigati, Ajeng Dwi Asti, "Prediction Questions for National Examination with Generalized Vector Space Model (GVSM) Algorithm", 2025 the 15th International Workshop on Computer Science and Engineering (WCSE 2025), pp. 70-77, Jeju Island, South Korea, June 28-30, 2025.