Sistem Rekomendasi Laptop Menggunakan Collaborative Filtering Dan Content-Based Filtering

Anderias Wijaya, Deni Alfian



Laptop is needed for students and for office workers because it is better than a desktop computer. In this era, laptops have a variety of brands and specifications that sometimes make people have difficulty in finding, choosing or buying the right laptop for their needs. Therefore there should be a recommendation system that can provide advice or recommendations, based on interest and needs in the search for references.

In commonly used algorithm recommendation system is collaborative filtering (CF) and content based filtering (CB). Collaborative filtering is a concept whereby the opinions of other users are used to predict items that a user might like / interest. For content based filtering using the availability of an item's content as a basis for recommendation.

In this research, the algorithm for collaborative filtering uses Adjusted-cossine similarity to calculate the similarity between user and weighted sum algorithm for prediction calculation, for content based filtering algorithm used is tf-idf to search availability of existing content.

This recommendation system combines collaborative filtering and content based filtering methods using mixed hybrid techniques, the system has also been tested using the blackbox method. The result of the required execution time is influenced by the number of items and content based filtering method has the fastest execution time compared to collaborative filtering and mixed hybrid methods.


Keywords : recommender system, collaborative filtering, content based filtering, mixed hybrid, Adjusted-cossine similarity, weightes sum.

Full Text:

PDF Remote PDF


Adomavicius, G., & Kwon, Y. (2015). Multi-criteria recommender systems. In Recommender systems handbook (pp. 847-880). Springer, Boston, MA.

Arifin, W. 2014. Implementasi hybrid (content based dan collaborative filtering) pada sistem rekomendasi software antivirus dengan multi-criteria rating. Fakultas ilmu komputer dan teknologi informasi, universitas sumatera utara. Medan.

Burke, R. (2007). Hybrid web recommender systems. In The adaptive web (pp. 377-408). Springer, Berlin, Heidelberg.

Handrico, A. (2012). Sistem rekomendasi buku perpustakaan fakultas sains dan teknologi dengan metode collaborative filtering. Jurusan teknik informatika, Fakultas sains dan Teknologi universitas Islam negeri Sultan Syarif Kasim Riau. Pekanbaru

Jannach, D., Karakaya, Z., & Gedikli, F. (2012, June). Accuracy improvements for multi-criteria recommender systems. In Proceedings of the 13th ACM conference on electronic commerce (pp. 674-689). ACM.

Ricci, F., Rokach, L., & Saphira, B. (2010). Introduction to recommender systems handbook. In F. Ricci, L. Rokach, B. Saphira, & P. B. Kantor (Eds.), Recommender systems handbook (pp. 1–29). New York: Springer.

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

Wibowo, A. (2010). Recommender System di Perpustakaan Universitas Kristen Petra menggunakan Rocchio Relevance Feedback dan Cosine Similarity. In Industrial Electronic Seminar.

Wiranto, E. (2010). Konsep Multicriteria Collaborative Filtering Untuk Perbaikan Rekomendasi. Seminar Nasional Aplikasi Teknologi Informasi (SNATI).


  • There are currently no refbacks.

Copyright (c) 2018

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