NDLI: Perbandingan Pengklasifikasi k-Nearest Neighbor dan Neighbor-Weighted k-Nearest Neighbor Pada Sistem Analisis Sentimen dengan Data Microblog

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Perbandingan Pengklasifikasi k-Nearest Neighbor dan Neighbor-Weighted k-Nearest Neighbor Pada Sistem Analisis Sentimen dengan Data Microblog

Content Provider	Semantic Scholar
Author	Martha, Miechelly Christanti, Valentina Naga, Dali S. Rompas, Parabelem Tinno Dolf
Copyright Year	2018
Abstract	Sistem analisis sentimen merupakan suatu sistem yang dibangun untuk menganalisis sentimen dalam bahasa Indonesia pada data yang diambil dari microblog, yaitu Twitter, Facebook, dan YouTube. Data tersebut diklasifikasikan menggunakan K-Nearest Neighbor (KNN) dan Neighbor-Weighted K-Nearest Neighbor (NW-KNN). Sistem analisis sentimen dibagi menjadi 4 (empat) tahap, yaitu tahap microblog crawling, tahap pra-pemrosesan data, tahap ekstraksi fitur, dan tahap klasifikasi sentimen. Data yang ada akan diklasifikasikan ke dalam 5 (lima) kelas, yaitu sangat positif, positif, netral, negatif, dan sangat negatif. Data tersebut kemudian akan dievaluasi menggunakan confusion matrix. Pengujian dilakukan dengan 50 data uji. NW-KNN memiliki akurasi yang paling tinggi di antara seluruh pengklasifikasi, yaitu 86% pada saat K = 9. Sedangkan untuk KNN, akurasi dari pengklasifikasi tersebut sebesar 82% pada saat K = 3. Kesimpulannya adalah NW-KNN berhasil mengatasi data latih dengan komposisi kelas yang tidak seimbang. Kata kunci: KNN, NW-KNN, Analisis Sentimen, Data Microblog
File Format	PDF HTM / HTML
DOI	10.36412/frontiers/001035e1/april201801.08
Volume Number	1
Alternate Webpage(s)	https://ejournal.unima.ac.id/index.php/efrontiers/article/download/219/188
Alternate Webpage(s)	https://doi.org/10.36412/frontiers%2F001035e1%2Fapril201801.08
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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