Perbandingan Rough Set dan Algoritma Apriori untuk Sistem Rekomendasi Perpustakaan

  • muhammad muhajir Universitas Islam Indonesia
  • Jaka Nugraha Universitas Islam Indonesia
  • Rachmad Febrian Universitas Islam Indonesia
Keywords: Recommendation System, Rough Set, Apriori Algorithm

Abstract

The recommendation system is a dynamic information filtering system that is produced according to user interests or behavior. The recommendation system in the library can provide book references based on user interests or characteristics. UII Central Library has an information system in the form of a database of book lending transactions that can be used for a recommendation system. The method is a rough set and apriori algorithm. This study compares 2 methods to get the best method that can be applied in the recommendation system. The results obtained by the number of rough set rules as many as 14 rules with an average coverage of 0,01111 and average accuracy of 0,87416 and the number of  apriori algorithm rules as many as 23 rules with an average support of 0,00276 and average confidence of 0,87458. Based on the number rules, the average value of accuracy or confidence, the apriori algorithm mehod is a method that can be used for the recommendation system in the UII Central Library.

References

[1] Isinkaye, F. O., Folajimi, Y. O., dan Ojokoh, B. A. (2015). Recommendation Systems: Principles, Methods and Evaluation. Egyptian Informatics Journal, 16(3), 261-273.
[2] Junarto. (2015). Perancangan dan Pembuatan Aplikasi Data Mining Berbasis Web Menggunakan Algoritma C.45 untuk Memprediksi Kelulusan Mahasiswa S-1 Pada Universitas Darma Persada. Skripsi, Fakultas Teknik, Universitas Darma Persada.
[3] Rissino, S., dan Lambert-Torres, G. (2009). Rough Set Theory—Fundamental Concepts, Principals, Data Extraction, and Applications. Data Mining and Knowledge Discovery in Real Life Applications. ISBN 978-90263-53-0. pp. 438, I-Tech.
[4] Komorowski, J., Pawlak, Z., Polkowski, L. dan Skowron, A. (2002). Rough Sets: A Tutorial. URL: alfa.mimuw.edu.pl/prace/1999/D5/Tutor06 09.ps.
[5] Agrawal, R., dan Srikant, R. (1994). Fast Algorithms for Mining Association Rules. Proc. 20th int. conf. Very Large Data Bases, VLDB. Vol. 1215, pp. 487-499.
[6] Gunadi, G., dan Sensuse, D.I. (2012). Penerapan Metode Data Mining Market Basket Analisis Terhadap Data Penjualan Produk Buku dengan Menggunakan Algoritma Apriori dan Frequent Pattern Growth (FP-Growth). Jurnal Telematika MKOM. Vol. 4, No. 1, Hal 118-132.
[7] Zhao, Y. (2011). R and data mining: Examples and case studies. R and Data Mining, 1-4.
Published
2019-01-19
How to Cite
muhajir, muhammad, Nugraha, J., & Febrian, R. (2019). Perbandingan Rough Set dan Algoritma Apriori untuk Sistem Rekomendasi Perpustakaan. UJMC (Unisda Journal of Mathematics and Computer Science), 4(2), 25 - 31. https://doi.org/https://doi.org/10.52166/ujmc.v4i2.1241