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Combination of rough set and cosine similarity approaches in student graduation prediction.

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Title: Combination of rough set and cosine similarity approaches in student graduation prediction.
Authors: Go, Ratna Yulika1 ratna.yulika@esaunggul.ac.id, Asianto, Tinuk Andriyanti1 tinuk.andriyanti@esaunggul.ac.id, Setiowati, Dewi1 dewi.setiowati@esaunggul.ac.id, Meilisa, Ranny1 meilisa.rannya@gmail.com, Munthe, Christine Cecylia2 Chri010@brin.go.id, Kusumawardhana, R. Hendra3 r.hendra.k@gmail.com
Source: International Journal of Electrical & Computer Engineering (2088-8708). Dec2025, Vol. 15 Issue 6, p6001-6011. 11p.
Subjects: Rough sets, Case-based reasoning, Higher education, Academic achievement
Abstract: Higher education institutions must deliver high-quality education that produces graduates who are knowledgeable, skilled, creative, and competitive. In this system, students are a vital asset, and their timely graduation rate is an important factor to consider. In the Department of Computer Science, a challenge arises in distinguishing between students who graduate on time and those who do not. With a low on-time graduation rate of just 1.90% out of 158 graduates, this issue could negatively affect the institution's accreditation evaluation. This research employs the case-based reasoning method, enhanced with an indexing process using rough sets and a prediction process utilizing cosine similarity. The testing, conducted using k-fold validation with 60%, 70%, and 80% of the data, produced average accuracy rates of 64.2%, 66.3%, and 65.6%, respectively. The test results indicate that the highest average accuracy of 66.3% was achieved with 70% of the cases. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:Higher education institutions must deliver high-quality education that produces graduates who are knowledgeable, skilled, creative, and competitive. In this system, students are a vital asset, and their timely graduation rate is an important factor to consider. In the Department of Computer Science, a challenge arises in distinguishing between students who graduate on time and those who do not. With a low on-time graduation rate of just 1.90% out of 158 graduates, this issue could negatively affect the institution's accreditation evaluation. This research employs the case-based reasoning method, enhanced with an indexing process using rough sets and a prediction process utilizing cosine similarity. The testing, conducted using k-fold validation with 60%, 70%, and 80% of the data, produced average accuracy rates of 64.2%, 66.3%, and 65.6%, respectively. The test results indicate that the highest average accuracy of 66.3% was achieved with 70% of the cases. [ABSTRACT FROM AUTHOR]
ISSN:20888708
DOI:10.11591/ijece.v15i6.pp6001-6011