Penerapan Particle Swarm Optimization Untuk Seleksi Fitur Pada Analisis Sentimen Review Jasa Maskapai Penerbangan Menggunakan Naive Bayes

Penerapan Particle Swarm Optimization Untuk Seleksi Fitur Pada Analisis Sentimen Review Jasa Maskapai Penerbangan Menggunakan Naive Bayes

• By Wati, Risa;
• Tesis
• 273
• 1-10

The quality of airline services can be seen from any opinions or reviews on passengers before. This reviewer classification grouped into positive opinion and a negative opinion. Data mining classification algorithm used is Naive Bayes are widely used in research because it serves well as a text classifier method however has the disadvantage that is very sensitive in the selection of features. Particle Swarm Optimization (PSO) Methods as a feature selection can solve optimization problems faster and more stable level of convergence. After testing the two models, namely models Naive Bayes algorithm and Naive Bayes algorithm based on the results obtained PSO is Naive Bayes algorithm produces an accuracy of 77.67% while for Naive Bayes algorithm based on PSO value amounted to 83.33% accuracy. Difference in value by 5.66% accuracy and included into the category of good classification.

• Katakunci Sentiment analysis, Review, Naive Bayes, Text Classification
• URL http://
• Penerbit ,

REFERENSI

[1]    Apriliyanti, A. (2010). Sentiment Analysis, 21, 1834–1848. Chen, J., Huang, H., Tian, S., & Qu, Y. (2009). Feature selection for text classification with Na??ve Bayes. Expert Systems with Applications, 36(3), 5432–5435.

[2]    Darujati, Cahyo dan Gumelar, Agustinus Bimo. (2012). Pemanfaatan Teknik Supervised Untuk Klasifikasi Teks Bahasa Indonesia. Universitas Narotama, Surabaya, 1858 – 4667.

[3]    Dehaff, M. (2010). Sentiment Analysis, Hard But Worth It!. [Online]. Tersedia di:http://www.customerthink.com/blog/sentiment_analysis_hard_but_worth_it.

[4]    Dephub. (2015). Lalu Lintas Angkutan Udara [Online] Tersedia di: http://hubud.dephub.go.id/?id/llu/index/filter:category,2;tahun,0;bulan,0;airport,0

[5]    Feldman, Ronen and Sanger, James. (2007). The Text Mining Handbook Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, New York.

[6]    Gorunescu, Florin (2011). Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer.

[7]    Haddi, E., Liu, X., & Shi, Y. (2013). The Role of Text Pre-processing in Sentiment Analysis. Procedia Computer Science, 17, 26–32.

[8]    Hamzah, A. (2014). Sentiment Analysis untuk Memanfaatkan Saran Kuesioner Dalam Evaluasi Pembelajaran Dengan Menggunakan Naive Bayes Classifier (NBC). Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) 2014 Yogyakarta, 15 November 2014 ISSN: 1979-911X, (November), 17–24.

[9]    He, Y., & Zhou, D. (2011). Self-training from labeled features for sentiment analysis. Information Processing and Management, 47(4), 606–616.

[10] Kang, H., Yoo, S. J., & Han, D. (2012). Senti-lexicon and improved Na??ve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications, 39(5), 6000–6010.

[11] Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(May), 1–167.

[12] Lu, Y., Liang, M., Ye, Z., & Cao, L. (2015). Improved particle swarm optimization algorithm and its application in text feature selection. Applied Soft Computing, 35, 629–636.

[13] Maimon, Oded & Rokach, Lior. (2010). Data Mining and Knowledge Discovey Handbook. New York: Springer.

[14] Markov, Z., & Daniel, T. (2007). Uncovering Patterns in.

[15] Pinheiro, R. H. W., Cavalcanti, G. D. C., Correa, R. F., & Ren, T. I. (2012). A global-ranking local feature selection method for text categorization. Expert Systems with Applications, 39(17), 12851–12857.

[16] Popescu, A. M., Etzioni, O.: Extracting Product Features and Opinions from Reviews, In Proc. Conf. Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, British Columbia, 2005, 339–346.

[17] Qingliang Miao, Qiudan Li, Ruwei Dai , “AMAZING: A sentiment mining and retrieval system”, Expert Systems with Applications 36 (2009) 7192–7198.

[18] Rodiyansyah, S. F., & Winarko, E. (2012). Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification. FMIPA UGM, 6(1), 91–100.

[19]   Salappa, A., Doumpos, M., & Zopounidis, C. (2007). Feature Selection Algorithms in Classification Problems: An Experimental Evaluation. System Analysis, Optimization and Data Mining in Biomedicine , 199-212.

[20] Sari, E. P., & Apriatni, E. P. (2014). Analisis Tingkat Kepuasan Pelanggan Dilihat Dari Dimensi Kualitas Pelayanan Dan Harga Pt . Garuda Indonesia Airlines (Persero), Tbk Kantor Cabang Semarang.

[21] Serrano-Guerrero, J., Olivas, J. a., Romero, F. P., & Herrera-Viedma, E. (2015). Sentiment analysis: A review and comparative analysis of web services. Information Sciences, 311, 18–38.

[22] Vercellis, Carlo (2009). Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate, Chichester, West Sussex: John Willey & Sons, Ltd.

[23]  Xue, H., Yang, Q., & Chen, S. (2009). SVM: Support Vector Machines. The Top Ten Algorithms in Data Mining (pp. 37 – 60).

[24]  Ye, Q., Zhang, Z., & Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36(3), 6527–6535.

[25]  Yun, L., Qiu-yan, C. & Hua, Z. (2011). Application of the PSO-SVM model for Credit Scoring. Seventh International Conference on Computational Intelligence and Security, 47-51.

[26]  Zhang, W., & Gao, F. (2011). An improvement to naive bayes for text classification. Procedia Engineering, 15, 2160–2164.

[27]       Zhang, Z., Ye, Q., Zhang, Z., & Li, Y. (2011). Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications.