SISTEM REKOMENDASI PEMBELAJARAN PADA E-LEARNING MENGGUNAKAN ALGORITMA CT-PRO
DOI:
https://doi.org/10.34151/technoscientia.v7i2.201Keywords:
Association Rules, CT-Pro, minimum supportAbstract
The progress of information technology in education, especially the use of e-learning in education institutions developed rapidly, SMA 2 Pamekasan was an example of schools that could use elearning. However, the teachers only used to take the value of the test, assignment by the teacher and to access course materials. Therefore, in this research use data that are considered essential to assist teachers measured students’s progress and help students answer the difficult questions. Subjects that used in this e-learning was chemical. Chemical was one subject that was considered difficult by most students. So, this application would help students solve a problems. The algorithm that used in this application was a ct-pro, it was algorithm of association rules to find combinations of data’s relations. The data were the wrong answers of 150 students. Questions consist of 400 numbers and 20 learning materials. In this research, the data was divided into 6, the data from 5 students, 30 students, 60 studenst, 90 students,120 students and 150 students. The results of this reseacrh was if there were more data, the combination formed itemset also be more and more, and the processing time will also be longer. However, if the greater the minimum support was entered, then the combination was formed will be less.
References
Giri, Sucahyo Yudho. And Gopalan, raj P. “CT-Pro : A Bottom-Up Non Recursive Frequent Itemset Mining Algorithm Using Compressed FP-Tree Data Structure ” Departement of computing, Curtin Univercity of technology kent st,bentley western australia 6102. 2005.
Ruldeviyani, yova and Fahrian, Muhammad, “implementasi algoritma-algoritma association rules sebagai bagian dari pengembangan data mining algorithms collection”: November 2008.
Hand, D., Mannila. H., Smyth, P. Principles of Data Mining. Cambridge: A Bradford Book. The MIT Press. 2001
Witten, I. H. and Frank, E. Data Mining: Practical Machine Learning Tools and Techniques 2nd Edition. Morgan Kaufmann Publisher. 2005
Ulmer, D. 2002. Mining an Online Auctions Data Warehouse. The Mid-Atlantic Student Workshop on Programming Languages and Systems. New York: Pace University.
Samuel, D. Penerapan Stuktur FP-Tree dan Algoritma FP-Growth dalam Optimasi Penentuan Frequent Itemset. Institut Teknologi Bandung. 2008.
Wahyuni, F. S., Siahaan, D. O., Fatichah. C. Penggunaan Cluster-Based Sampling Untuk Penggalian Kaidah Asosiasi Multi Obyektif. KURSOR. 5: 59-67. 2009.
Kurnia, Parama Fadli. 2010. “ innovation of information technology : algoritma ct-pro” . URL: http://paramafadlikurnia.blogspot.sg/2010/12/algoritma-ct-pro_5.html, diakses 09 maret 2013.
