Analisa Multithreading Pada Sistem Rekomendasi Menggunakan Metode Collaborative Filtering Dengan Apache Mahout
DOI:
https://doi.org/10.33633/tc.v17i1.1593Keywords:
Apache Mahout, Collaborative Filtering, MultithreadingAbstract
Apache mahout saat ini melakukan pengembangan sistem rekomendasi yang didalamnya menggunakan metode Collaborative Filtering, namun dalam implementasinya masih memiliki kekurangan didalam waktu pemrosesan yang masih memakan waktu cukup lama untuk memproses data yang berukuran besar. Penelitian ini akan memanfaatkan multithreading untuk mempercepat waktu pemrosesan data menggunakan library Apache Mahout. Dalam penelitian ini, diketahui bahwa adanya multithreading mampu meningkat kinerja dalam pemrosesan waktu eksekusi data pada Apache Mahout. Pengujian yang telah dilakukan dengan jumlah 20 juta data, didapatkan hasil pengujian pada single thread dengan lama waktu pemrosesan datanya selama 2218 detik. Dan pada pengujian untuk 4 thread didapatkan hasil 764 detik, dan kemudian untuk 8 thread didapatkan hasil 691 detik dan pada pengujian untuk 16 thread didapatkan hasil 1097 detik. Dari berapa pengujian yang telah dilakukan telah membuktikan bahwa multithreading mampu meningkatkan kinerja apache mahout dalam sistem rekomendasi asalkan jumlah thread tidak melebihi kapasitas ukuran thread yang ada di processor.References
J. Pardede, “Implementasi multithreading untuk Meningkatkan Kinerja Information Retrieval Dengan Metode GVSM,†JSISKOM, vol. Vol. 4, No, 2014.
E. A. Laksana, “Collaborative Filtering dan Aplikasinya,†vol. 1, no. 1, pp. 36–40, 2014.
U. Farooque, “Implementing User Based Collaborative Filtering to Build a Generic Product Recommender Using Apache Mahout,†INDIACom, pp. 984–987, 2016.
M. Hameed, “Collaborative Filtering Based Recommendation System: A survey.,†… J. Comput. …, vol. 4, no. 5, pp. 859–876, 2012.
X. Daoping, Z. Alin, and L. Yubo, “A Parallel Clustering Algorithm Implementation Based on Apache Mahout,†2016 Sixth Int. Conf. Instrum. Meas. Comput. Commun. Control, pp. 790–795, 2016.
T. S. Kumar and S. Pandey, “Customization of recommendation system using collaborative filtering algorithm on cloud using mahout,†Adv. Intell. Syst. Comput., vol. 321, pp. 39–43, 2015.
S. K. Mahapatra, S. K. Mohapatra, S. Mahapatra, and S. K. Tripathy, “A Proposed Multithreading Fuzzy C-Mean Algorithm for Detecting Underwater Fishes,†2016 2nd Int. Conf. Comput. Intell. Networks, pp. 102–105, 2016.
A. Kurniawan, “Sistem Rekomendasi Produk Sepatu Dengan Menggunakan,†vol. 2016, no. Sentika, pp. 18–19, 2016.
A. Jabakji and H. Dag, “Improving item-based recommendation accuracy with user’s preferences on Apache Mahout,†Proc. - 2016 IEEE Int. Conf. Big Data, Big Data 2016, no. 978, pp. 1742–1749, 2016.
H. Mohanty , Soumendra; Jagadeesh, Madhu; Srivatsa, Big Data Imperatives Enterprise Big Data Warehouse, BI Implementations and Analytics. Aprees, 2013.
Hongyi Su, Xianfei Lin, Caiqun Wang, Bo Yan, Hong Zheng; Parallel Collaborative Filtering Recommendation Model Based on Expand-Vector; International Conference on Intellegent Computing, pp 1-10
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