Faculty informatics school of computing, Telkom University, Indonesia
BibTex Citation Data :
@article{JTSISKOM14558, author = {Aditya Mahendra Zakaria and Erwin Budi Setiawan}, title = {Aspect-Based Analysis of Telkomsel User Sentiment on Twitter Using the Random Forest Classification Method and Glove Feature Expansion}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {10}, number = {2}, year = {2022}, keywords = {sentiment analysis, classification, twitter , random forest , glove feature expansion}, abstract = {In this modern era, people certainly very easy to access social media, one of which is Twitter. Twitter is usually used by the public in expressing opinions regarding current issues, product reviews, and many other things positive, negative, or neutral opinions, or can be interpreted as sentiment. This study aims to analyze the aspect-based sentiment of Telkomsel users on Twitter using random forest classification and the extension of the Glove feature. This study uses signal aspects and service aspects with a total dataset of 16988 data. A Random forest can be classified as relevant and accurate for sentiment analysis with the greatest accuracy of 80.37% in the signal aspect and 80.12% in the service aspect, and the expansion feature is proven to be able to increase the performance value of this study by 13.15% in the signal aspect. and 5.37% in the service aspect.}, issn = {2338-0403}, pages = {114--119} doi = {10.14710/jtsiskom.2022.14558}, url = {https://jtsiskom.undip.ac.id/article/view/14558} }
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