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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Giyanti Giyanti
Department of Mathematics Education, Universitas Serang Raya, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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CLASSYFYING STUDENTS DECISION MAKING ABILITY USING K-NEAREST NEIGHBOR FOR DETERMINING STUDENTS SUPPLEMENTARY LEARNING Giyanti Giyanti; Rina Oktaviyanthi; Usep Sholahudin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.084 KB) | DOI: 10.30598/barekengvol17iss1pp0559-0570

Abstract

Mathematical decision-making ability is a complex cognitive process of finding problems solutions which is continuously explored and optimized for undergraduate students. The current research only focus on categorization on class score average into high, medium and low abilities. As a result, the lecturers do not have any standard categories to classify students’ abilities as a reference in planning supplementary learning that could optimize undergraduate students’ abilities. Therefore, the purpose of this study is to determine a classification model for undergraduate students' mathematical decision-making abilities that require supplementary learning based on the identification of the shortest distance of a new data from an existing data directory. The research method involved data mining techniques with the KNN classification model through the Knowledge Discovery in Database (KDD) process starting from data selection, pre-processing, transformation, data mining and interpretation/evaluation [1]. A total of 100 data were used as research samples which were divided into training data and testing data. Based on the test results, it is obtained that the accuracy of the classification model is 95% for the parameter value k = 15, meaning that each predicted testing data for the classification class is close to the actual condition with the number of neighbors 15 data from the training data.
Co-Authors Rina Oktaviyanthi Usep Sholahudin