Open AccessModel Prediksi Penempatan Magang Siswa SMK menggunakan Teknik Association Rule Mining
Author(s) -
Dwi Welly Sukma Nirad,
Afriyanti Dwi Kartika,
Aghill Tresna Avianto,
Aulia Anshari Fathurrahman
Publication year - 2020
Publication title -
indonesian journal of computer science/indonesian journal of computer science
Language(s) - English
Resource type - Journals
eISSN - 2549-7286
pISSN - 2302-4364
DOI - 10.33022/ijcs.v9i1.216
Subject(s) - internship , association rule learning , vocational education , association (psychology) , vocational school , mathematics education , computer science , psychology , medical education , pedagogy , data mining , medicine , psychotherapist
Insternship activity is one of the core activities of every Vocational School (SMK) as the purpose of this school is to conduct education at the level of work-oriented readiness. Every SMK graduate is expected to be better prepared to enter the industrial world. However, in fact there were gaps that resulted in the unpreparedness of students after graduating from school. This research identified and analyzed the placement of student internships. The aim was to find an insternship placement pattern in order to get an overview and recommendation of an appropriate internship according to students abilities. The technique used was the association rule mining, a technique of the data mining method that was useful for uncovering the rules that were correlated to each other so that they can better organize and predict the internship placements. The results showed that the association rule mining could be applied to analyze student performance and predict internship placements in the future. This prediction could be a consideration for the teacher to determine the subjects that need to be improved to prepare students for internships.