Use Clustering Data of Student High School for Placement in Personalization E-Learning on Higher Education
Purwono Hendrad1, Harry Budi Santoso2, Zainal A Hasibuan3
1Purwono Hendradi,M.Kom Teknik Informatika Universitas Muhammadiyah Magelang, Jl. Mayjend Bambang Soegeng, Magelang, Indonesia.
2Harry Budi Santoso, M.Kom, Ph.D, Ilmu Komputer, Universitas Indonesia Depok Jawa Barat, Indonesia.
3Prof. Zainal A Hasibuan, Ilmu Komputer, Universitas Indonesia Depok Jawa Barat, Indonesia.
Manuscript received on April 02, 2017. | Revised Manuscript received on April 10, 2017. | Manuscript published on April 15, 2017. | PP: 7-12 | Volume-4, Issue-10, April 2017. | Retrieval Number: J10250441017/2017©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Personalize the e-learning begins after students interact with the system by utilizing the functions and features to collect data and process it so that the resulting information from students who used to organize further activities. In another study, the educational background of the student (and types of SMA) also affects the success in education at the university. In this study developed a personalized e-learning design of the early, which is when the new students will interact with the system. The system will be a kind of student placement test. The case studies used subjects Program Building which is one of the core subjects in the study program Engineering Informatics. As the methods used Knowledge Data Discovery (KDD) using background data combined with a high school student math scores on the National Exam as an ingredient on the stage of Data Mining. This study will measure the extent of the student’s educational background above can be used as a system of placement of students in personalized e-learning.
Keywords: High school background, data mining, placement, personalized e-learning.