Text Mining Untuk Analisis Sentimen Review Film Menggunakan Algoritma K-Means

Authors

  • Setyo Budi Universitas Dian Nuswantoro Semarang

DOI:

https://doi.org/10.33633/tc.v16i1.1263

Abstract

Kemudahan manusia didalam menggunakan website mengakibatkan bertambahnya dokumen teks yang berupa pendapat dan informasi. Dalam waktu yang lama dokumen teks akan bertambah besar. Text mining merupakan salah satu teknik yang digunakan untuk menggali kumpulan dokumen text sehingga dapat diambil intisarinya. Ada beberapa algoritma yang di gunakan untuk penggalian dokumen untuk analisis sentimen, salah satunya adalah K-Means. Didalam penelitian ini algoritma yang digunakan adalah K-Means. Hasil penelitian menunjukkan bahwa akurasi K-Means dengan dataset digunakan 300 positif dan 300 negatif  akurasinya 57.83%,  700 dokumen positif dan 700  negatif akurasinya 56.71%%, 1000 dokumen positif dan 1000  negatif akurasinya 50.40%%. Dari hasil pengujian disimpulkan bahwa semakin besar dataset yang digunakan semakin rendah akurasi K-Means.   Kata Kunci : Text Mining, Analisis Sentimen, K-Means, Review Film 

References

Witten, I. H., Frank E., Hall, M. A., 2011, Data Mining, Practical Machine Learning Tools and Techniques, Ed. 3, Burlington: Morgan Kaufmann, USA.

Francis, L, Flynn, M., 2010, Text Mining Handbook, Casualty Actuarial Society E-Forum, p.1, Spring.

G. Li and F. Liu, 2010, A Clustering-based Approach on Sentiment Analysis, in Intelligent Systems and Knowledge Engineering (ISKE), 2010 International Conference on 2010 Nov 15, pp.331-337, Australia, IEEE

Stylios, G., Christodoulakis, D., Besharat, J., Vonitsanou, M., Kotrotsos, I., Koumpouri, A. and Stamou, S., 2010. Public opinion mining for Governmental Decisions. Electronic Journal of e-Government, Vol. 8(2), pp.203-214.

Keefe, T. O., Koprinska, I., 2009, Feature Selection and Weighting Methods in Sentiment Analysis, Proceedings of the 14th Australasian Document Computing Symposium, pp. 1-8, Dec 4.

Abbasi, A., Chen, H., Salem, A., 2008. Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums. ACM Transactions on Information Systems (TOIS), 26(3), p.12.

Pang, B., Lee, L., 2004, A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts, ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Barcelona, Spain, July 21.

L. Suanmali, L., N. Salim, N. dan Binwahlan, M.S., 2008, Automatic Text Summarization Using Feature Based Fuzzy Extraction, Jurnal Teknologi Maklumat, Jilid 20, Bil.2, Desember.

Baeza-Yates, R. and Ribeiro-Neto, B., 1999. Modern information retrieval (Vol. 463). New York: ACM press.

MacQueen, J., 1967, June. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297).

Velmurugan, T. and Santhanam, T., 2010. Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points. Journal of computer science, 6(3), p.363.

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Published

2017-02-06

Issue

Vol. 16 No. 1 (2017): Februari 2017

Section

Articles

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Copyright (c) 2017 Setyo Budi

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