Sistem Rekomendasi Film Menggunakan Metode K-NN

Authors

  • Mohammad Amir Fanani Universitas Pembangunan Nasional “Veteran” Jawa Timur

DOI:

https://doi.org/10.55606/juisik.v4i1.760

Keywords:

Accuracy, K-Nearest Neighbors, Precision, Recal, Recommendation systems

Abstract

Recommendation systems are at the heart of cutting through the noise of online data, helping users find content that matches their preferences. In this regard, the K-Nearest Neighbors (KNN) method stands out as a promising approach. KNN, as a similarity-based algorithm, utilizes information from nearest neighbors to make predictions or recommendations. This research explores the implementation of KNN in developing a film recommendation system with a focus on increasing the accuracy and relevance of recommendations. Related references, such as the evaluation of recommendation systems by Herlocker et al. (2004) and the concept of collaborative filtering by Resnick and Varian (1997), are the basis for understanding and improving the potential of KNN. By detailing the exploration of this concept, it is hoped that this research will provide a comprehensive and holistic view in the development of a film recommendation system. From testing using the performance test of the KNN method, namely accuracy, recall, and precision with the best value of 45.4% for accuracy, 45.4% for recall, and 100% precision so that the algorithm can be applied in the film recommendation system.

References

Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53.Risdwiyanto, A. & Kurniyati, Y. (2015). Strategi Pemasaran Perguruan Tinggi Swasta di Kabupaten Sleman Yogyakarta Berbasis Rangsangan Pemasaran. Jurnal Maksipreneur: Manajemen, Koperasi, dan Entrepreneurship, 5(1), 1-23. http://dx.doi.org/10.30588/SOSHUMDIK.v5i1.142

Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295).

Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40(3), 77-87.

Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (pp. 43-52).

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Published

2024-01-09

How to Cite

Mohammad Amir Fanani. (2024). Sistem Rekomendasi Film Menggunakan Metode K-NN. Jurnal Ilmiah Sistem Informasi Dan Ilmu Komputer, 4(1), 178–185. https://doi.org/10.55606/juisik.v4i1.760