Open AccessImplementation K-nearest neighbour for student expertise recommendation system
Author(s) -
Ichsan Taufik,
Yana Aditia Gerhana,
A I Ramdani,
Mohamad Irfan
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1402/7/077004
Subject(s) - confusion matrix , confusion , field (mathematics) , computer science , sample (material) , test (biology) , artificial intelligence , matrix (chemical analysis) , data mining , machine learning , data science , psychology , mathematics , paleontology , chemistry , materials science , chromatography , psychoanalysis , pure mathematics , composite material , biology
The ability of students to determine their chosen field of expertise is still subjective, many students choose the field of expertise because their classmates choose the field of expertise not by considering their abilities and interests. This research uses the KNN classification method to determine areas of expertise that are in accordance with student expertise. The KNN method was chosen because it is a method that uses supervised algorithms where the results of new query instances are classified based on the majority of the categories in the KNN whose purpose is to classify test data based on training data. This system was tested using the confusion matrix method and the results were 98.30% of the total student data sample of 30 people.