Klasifikasi Multilabel Menggunakan Metode Fuzzy Similarity K-Nearest Neighbor Untuk Rekomendasi Pencarian Artikel Online - Wahyuni Lubis; Yuita Arum Sari; Mochammad Ali Fauzi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
The article is someone's opinion of the paper that addresses a specific problem that is actual and sometimes controversial to inform, influence, persuade, and entertain the reader. Rapid technological developments led to the large number of articles written online. Each article has a different label online, and allows each article has more than one label. The number of online articles that exist on the internet every day growing which makes the reader's difficulty in finding the desired information. The proper classification can improve the quality of information retrieval. A method of Fuzzy K-Nearest Neighbor Similarity is a method that combines the multilabel classification method of Fuzzy Similarity Measure and MLKNN. Previous research on method FSKNN has better speed in doing computing k nearest neighbors and better performance of the method MLKNN. The steps undertaken in this research is conducting a text preprocessing, document clustering, weighting, classification and search process. On the research of the optimal values obtained this F1 and BEP amounted to 0.933 and 0.937 at k = 1 and alpha = 0.5. On the recommendation of the search articles online using the method FSKNN obtained the highest precision value of 0.5 and 0.8 recall. From the results of F1 and the BEP obtained, indicating that the method FSKNN was kind enough to do a multilabel classification articles online.