Determination of Film Recommendations using the Generalized Sequence Pattern (GSP) Association Method

Authors

  • Acep Razif Andriyan Teknik Informatika, UIN Sunan Gunung Djati Bandung, Indonesia
  • Dinda Meysya Rochma Teknik Informatika, UIN Sunan Gunung Djati Bandung, Indonesia
  • Melani Nur Mudyawati Teknik Informatika, UIN Sunan Gunung Djati Bandung, Indonesia
  • Miftahul Jannah Teknik Informatika, UIN Sunan Gunung Djati Bandung, Indonesia
  • Siti Lufia Dwi Agustini Teknik Informatika, UIN Sunan Gunung Djati Bandung, Indonesia
  • Arham Aulia Nugraha Teknik Informatika, UIN Sunan Gunung Djati Bandung, Indonesia

Keywords:

Data Mining, Generalized Sequential Pattern, Association Mining, film recomendation

Abstract

Along with the development of the times, films are used as a medium of communication and as a medium of entertainment that can be watched by fans everywhere. The high level of film enthusiasts makes viewing patterns that can be used as recommendation data. Besides, this system can provide benefits to the company concerned. This study used the Generalized Sequential Pattern (GSP) Algorithm with the Association Mining method. The primary function is to find the sequence or sequential of a set of attributes that often appear together to see the results to find out the recommendation data information after the previous film runs out.

Downloads

Download data is not yet available.

References

F. H. Gunawan, Alex Xandra Albert Sim, M Hawari Simanullang, M Firkhan Siregar, “Pengembangan Aplikasi Market Basket Analysis Menggunakan Algoritma Generalized Sequential Pattern pada Supermarket,†in Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 2015, pp. 1–6, [Online]. Available: https://journal.uii.ac.id/Snati/article/view/3527.

E. Muningsih, “PENERAPAN METODE ASOSIASI GSP DAN APRIORI UNTUK STOK DAN REKOMENDASI PRODUK,†Konf. Nas. Ilmu Sos. Teknol., pp. 412–416, 2017.

A. Malau, E. Buulolo, and K. Ulfa, “APLIKASI DATA MINING MENGGUNAKAN ALGORITMA GENERALIZED SEQUENTIAL PATTERN (GSP) UNTUK MENENTUKAN ATURAN ASSOSIASI PADA TRANSAKSI PEMINJAMAN BUKU DI PERPUSTAKAAN STMIK BUDIDARMA MEDAN,†Pelita Inform. Inf. dan Inform., vol. 6, no. 4, pp. 206–209, 2018.

J. Han, J. Pei, Y. Yin, and R. Mao, “Mining frequent patterns without candidate generation: A frequent-pattern tree approach,†Data Min. Knowl. Discov., vol. 8, no. 1, pp. 53–87, 2004, doi: 10.1023/B:DAMI.0000005258.31418.83.

F. Gorunescu, Data Mining: Concepts, models and techniques. 2011.

R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,†J. Comput. Sci. Technol., vol. 15, no. 6, pp. 487–499, 1994, doi: 10.1007/BF02948845.

R. Srikant and R. Agrawal, “Mining sequential patterns: Generalizations and performance improvements,†in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1996, vol. 1057 LNCS, pp. 3–17, doi: 10.1007/bfb0014140.

Downloads

Published

2021-02-12

Issue

Vol. 3 (2021): Mini Seminar Kelas Data Mining 2020

Section

Articles

Citation Check

License

Copyright (c) 2021 Acep Razif Andriyan, Dinda Meysya Rochma, Melani Nur Mudyawat, Miftahul Jannah, Siti Lufia Dwi Agustini, Arham Aulia Nugraha

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.