KLASIFIKASI SENTIMEN PEMBELI BERDASARKAN LAYANAN SMS “SUARA KONSUMEN”  TERHADAP PRODUK MENGGUNAKAN METODE K-NN

David Addiwijaya Hanz; Budi Susanto; Lukas Chrisantyo

doi:10.21460/inf.2015.111.423

KLASIFIKASI SENTIMEN PEMBELI BERDASARKAN LAYANAN SMS “SUARA KONSUMEN” TERHADAP PRODUK MENGGUNAKAN METODE K-NN

David Addiwijaya Hanz, Budi Susanto, Lukas Chrisantyo

Abstract

Limited number of characters in an SMS causes the use of words and the structure of solid and compact but obviously become very important in presenting the content and purpose of the sender of the SMS. This research studied how the use of SMS classify structures and limited words to get the sentiment of an SMS service "Suara Konsumen" through text mining approach. This study uses K-NN algorithm in the classification, besides the K-NN algorithm of this study will be weighted words using TF-IDF methods, Feature Selection in the selection of words also Cosine Similiarity in measuring the degree of proximity between documents. In determining the success, measured the accuracy of the documents the trial and the results are very accurate with an average value of 89% accuracy on the variation of K and Feature Selection.

Keywords

K-Nearest Neighbor, Sentiment Analysist, Document Classification, Short Message Service, Cosine Similiarity

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References

El-Halees, A. (2011). Arabic Opinion Mining using Combined Classification Approach.

Han, J., Kamber, M., & Pei, J. (2012). Data Mining : Concepts and Techniques. Morgan Kaufmann.

Handoko, B. Y. (2008). Perbandingan Metode Naive Bayes Classifier (NBC) dengan K-Nearest Neighbor (K-NN) untuk Klasifikasi Dokumen.

Hu, M., & Liu, B. (2004). Mining and Summarizing Customer Reviews.

Kowalski, G. (2011). Information Retrieval Architecture and Algorithms. Ashburn: Spinger.

Kusumo, G. D. (2013). Klasifikasi Sentimen Pembaca berdasar Komentar terhadap Berita menggunakan Metode Naive Bayes Classifier.

Liu, B. (2012). Sentiment Analysis and Opinion Mining . Morgan & Claypool Publishers.

Liu, B. (2007). Web Data Mining : Exploring Hyperlinks, Contents, and Usage Data. Chicago: Springer.

Pang, B., Lee, L., & Vaithyanathan, S. (2003). Thumbs up? Sentiment Classification using Machine Learning.

Wiebe, J., Bruce, R., & O'Hara, T. (1999). Development and use of a gold standard data set for. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL-99) , 246–253.

DOI: http://dx.doi.org/10.21460/inf.2015.111.423

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