Analisis Sentimen Kurikulum 2013 Pada Sosial Media Twitter Menggunakan Metode K-Nearest Neighbor dan Feature Selection Query Expansion Ranking - Nurul Dyah Mentari; Mochammad Ali Fauzi; Lailil Muflikhah (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Kurikulum 2013 has become a hot topic that is often discussed by society on Twitter. Twitter is one of the social media that used by a society to talk about a particular subject. This study attempted to analyze tweets about the Kurikulum 2013 by classifying whether it is a positive opinion or a negative opinion. Classification process is done by K-Nearest Neighbor method by using Query Expansion Ranking method for feature selection. There are 4 main processes in this analysis sentiment system that first is text pre-processing, term weighting (TF-IDF), feature selection, and classification. Based on the tests in this study proven that feature selection improve accuracy of systemresults. The best acuracy results of 96.36%was obtained when k = 1 and using a feature selection of 50% ratio. The test results by using selection feature of 50% ratio get higher accuracy than a system does not use the selection feature because some noise features that have been removed.