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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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KOMPARASI ALGORITMA KLASIFIKASI TEXT MINING UNTUK ANALISIS SENTIMEN PADA REVIEW RESTORAN
Keywords:
Sentiment Analysis, Data Mining, Naïve Bayes, Support Vector Machine
Abstract
Situs review online terus bertambah populer karena semakin banyak orang mencari saran dari sesama pengguna mengenai layanan dan produk. Sejumlah penelitian beberapa tahun terakhir juga sudah berkembang dalam bidang analisis sentimen guna menemukan solusi yang tepat dalam membuat sistem yang dapat secara otomatis menganalisis review di intenet dan mengekstrak informasi yang paling relevan bagi pengguna. Dalam penelitian sebelumnya mengenai analisis sentimen pada review restoran, akurasi algoritma Naive Bayeslebih unggul dari Support Vector Machine. Pada penelitian ini digunakan dua algoritma, yakni Naïve Bayes dan Support Vector Machine. Tujuannya adalah untuk menentukan algoritma terbaik yang bisa digunakan untuk data review teks bahasa Indonesia. Dari hasil pengolahan data, algoritma Naïve Bayes lebih unggul dari Support Vector Machine dengan tingkat akurasi sebesar 87%. Sedangkan algoritma Support Vector Machine hanya menghasilkan akurasi 56%. Penulis membuat aplikasi analisis sentiment menggunakan bahasa pemrograman Java sebagai penunjang penelitian.
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References
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Basari, A. S. H., Hussin, B., Ananta, I. G. P., & Zeniarja, J. (2013). Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization. Procedia Engineering, 53, 453–462. https://doi.org/10.1016/j.proeng.2013.02.059
Brody, S. (2010). An Unsupervised Aspect-Sentiment Model for Online Reviews, (June), 804–812.
Chen, J., Huang, H., Tian, S., & Qu, Y. (2009). Feature selection for text classification with Naïve Bayes. Expert Systems with Applications, 36(3), 5432–5435. https://doi.org/10.1016/j.eswa.2008.06.054
He, Y., & Zhou, D. (2011). Self-training from labeled features for sentiment analysis. Information Processing & Management, 47(4), 606–616. https://doi.org/10.1016/j.ipm.2010.11.003
Muthia, D. A. (2018). Laporan Akhir Penelitian Dosen Yayasan. Jakarta.
Uysal, A. K., & Gunal, S. (2012). A novel probabilistic feature selection method for text classification. Knowledge-Based Systems, 36, 226–235. https://doi.org/10.1016/j.knosys.2012.06.005
Waila, P., Singh, V. K., & Singh, M. K. (2012). Evaluating Machine Learning and Unsupervised Semantic Orientation approaches for sentiment analysis of textual reviews. 2012 IEEE International Conference on Computational Intelligence and Computing Research, (c), 1–6. https://doi.org/10.1109/ICCIC.2012.6510235
Wang, G., Sun, J., Ma, J., Xu, K., & Gu, J. (2014). Sentiment classification: The contribution of ensemble learning. Decision Support Systems, 57, 77–93. https://doi.org/10.1016/j.dss.2013.08.002
Ye, Q., Zhang, Z., & Law, R. (2009). Expert Systems with Applications Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems With Applications, 36(3), 6527–6535. https://doi.org/10.1016/j.eswa.2008.07.035
Zhang, Z., Ye, Q., Zhang, Z., & Li, Y. (2011). Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications, 38(6), 7674–7682. https://doi.org/10.1016/j.eswa.2010.12.147
Published
2018-03-15
How to Cite
Muthia, D. (2018). KOMPARASI ALGORITMA KLASIFIKASI TEXT MINING UNTUK ANALISIS SENTIMEN PADA REVIEW RESTORAN. Jurnal Pilar Nusa Mandiri, 14(1), 69-74. https://doi.org/10.33480/pilar.v14i1.92
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