PENGARUH USER PROFILING PADA REKOMENDASI SISTEM MENGGUNAKAN K MEANS DAN KNN

Authors

  • Hartatik Universitas Amikom Yogyakarta
  • Rosyid Universitas AMIKOM Yogyakarta

DOI:

https://doi.org/10.24076/joism.2020v2i1.199

Keywords:

Sparsity, Skalabilitas, Silhouette, k means, KNN

Abstract

Sparsity adalah satu masalah yang sering terjadi pada teknik collaborative clustering dimana user sedikit sekali memiliki informasi (pada penelitian ini rating) yang menyebabkan sistem seringkali tidak akurat ketika memberikan rekomendasi. Banyak metode yang bisa digunakan untuk menyelesaikan masalah sparsity data, salah satunya adalah metode KNN. Namun metode KNN memiliki kelemahan yaitu scalability. Scalability terjadi ketika ketika data yang harus dicari kesamaannya semakin besar. Salah satu solusi yang mungkin diimplementasikan adalah dengan mencari profil dari user dan mengelompokkannya menjadi satu kelompok. Eksperimen yang dilakukan pada penelitian ini untuk mengatasi masalah sparsity dan scalability adalah dengan menggabungkan algoritma silhouette, k-means, K-Nearest Neighbour. Dataset yang dipakai di penelitian ini, berjumlah 700 rating yang di crawling melalui web traveloka. Data rating antara user dan item akan disimpan dalam database, untuk selanjutnya dirubah menjadi bentuk array user-item. Hasil pengujian dengan 5 data uji didapatkan nilai rata-rata RMSE 1,33% dengan rata-rata akurasi = 100% - 1,33% = 98,67%.

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References

F.O. Isinkaye, Y.O. Folajimi, B.A. Ojokoh, Recommendation systems: Principles, methods and evaluation, Egyptian Informatics Journal Volume 16, Issue 3, November 2015, Pages 261-273.

S. Schiaffino, A. Amandi, Intelligent User Profiling, M. Bramer (Ed.): Artificial Intelligence, LNAI 5640, pp. 193 – 216, 2009.

J.S. Breese, D. Heckerman, C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, July 1998, Pages 43–52.

A.F Jain, S.K Vishwakarma, P. Jain, An Efficient Collaborative Recommender System for Removing Sparsity Problem, ICT Analysis and Applications pp 131-141

J. Cheng, L. Zhang, Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving Data Sparsity, Pacific-Asia Conference on Knowledge Discovery and Data Mining PAKDD 2019: Advances in Knowledge Discovery and Data Mining pp 369-380

J.P Ortega, N.N.A Ortega, D. Romero, Balancing effort and benefit of K-means clustering algorithms in Big Data realms, PLoS ONE 13(9): 2018

U. Ku?elewska, Clustering Algorithms in Hybrid Recommender System on MovieLens Data, STUDIES IN LOGIC, GRAMMAR AND RHETORIC 37 (50) 2014

Theodoridis, S., Pikrakis, A., Koutroumbas, K., Cavouras, D. (2010). An Introduction to Pattern Reccognition : A MATLAB Approach. Academic Press, USA

S. Awawdeh , A. Edinat, A. Sleit, An Enhanced K-means Clustering Algorithm for Multi-attributes Data, International Journal of Computer Science and Information Security (IJCSIS), Vol. 17, No. 2, February 2019

Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics. 1987, Vol.20, pp.53-65.

Kwedlo, W. A clustering method combining differential evolution with the k-means algorithm. Pattern Recognition Letters. 2011, Vol.32, pp.1613–1621.

M A Syakur, B K Khotimah, E M S Rochman, B D Satoto, Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster, IOP Conf. Series: Materials Science and Engineering 336 (2018) 012017 doi:10.1088/1757-899X/336/1/012017

T. Thinsungnoena, N. Kaoungkub, P. Durongdumronchaib, K. Kerdprasopb, N. Kerdprasopb, The Clustering Validity with Silhouette and Sum of Squared Errors, Proceedings of the 3rd International Conference on Industrial Application Engineering 2015

Zhang, D., Hsu, C.H., Chen, M., Chen, Q., Xiong, N., Lloret, J.: Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems. IEEE Trans. Emerg. Top. Comput. 2(2), 239–250 (2014)

Xie, F., Xu, M., Chen, Z.: RBRA: a simple and efficient rating-based recommender algorithm to cope with sparsity in recommender systems. In: International Conference on Advanced Information NETWORKING and Applications Workshops, pp. 306–311 (2012)

Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, US (2011). https://doi.org/10.1007/978-0-387-85820-3

A. Gong, Y. Gao, Z. Gao, W. Gong, H. Li, H. Gao, A Slope One and Clustering based Collaborative Filtering Algorithm, International Journal of Hybrid Information Technology Vol.9, No.4 (2016), pp. 437-446

D.A. Adeniyi, Z. Wei, Y. Yongquan, Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method, Applied Computing and Informatics Volume 12, Issue 1, January 2016, Pages 90-108.

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Published

2020-07-04

How to Cite

Hartatik, H., & Rosyid, R. (2020). PENGARUH USER PROFILING PADA REKOMENDASI SISTEM MENGGUNAKAN K MEANS DAN KNN. Journal of Information System Management (JOISM), 2(1), 13-18. https://doi.org/10.24076/joism.2020v2i1.199

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