K-Means and K-NN Methods For Determining Student Interest

International Journal of artificial intelligence research

View Publication Info
 
 
Field Value
 
Title K-Means and K-NN Methods For Determining Student Interest
 
Creator Guslendra, Guslendra
Defit, Sarjon
Bastola, Ramesh
 
Subject Artificial Intelligence, software engginering
K-MeanS, K-NN, Specialization, Student interest
 
Description Putra Indonesia University 'YPTK' Padang's Department of Information Systems, Faculty of Computing Science has three specializations, namely Information Technology Management, Business Information Systems, and Industrial Information Systems. In the fifth semester, the acquisition of specializations takes place. In the next semester, the selection of specialist programs will be determined. The option of the degree is adapted to students' needs and capacities. The acquisition of results generated in the previous semester can be seen. The objective of this survey is to provide students with suggestions for the collection of degrees. The study was performed using K-Means and K-Nearest Neighbor methods to obtain the classification of students and the correlation between recent cases and past cases. This analysis uses 13 characteristics, of which 12 are predictors and 1 is the option. The test results can be used as a way to suggest the student preferences based on preset attributes through the K-Means and K-NN methods.
 
Publisher STMIK Dharma Wacana
 
Contributor
 
Date 2021-07-23
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Identifier http://ijair.id/index.php/ijair/article/view/222
10.29099/ijair.v6i1.222
 
Source International Journal of Artificial Intelligence Research; Vol 6, No 1 (2022): Articles in press
2579-7298
10.29099/ijair.v6i1
 
Language en
 
Rights Copyright (c) 2021 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0
 

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