Seleksi Fitur Berbasis Pearson Correlation Untuk Optimasi Opinion Mining Review Pelanggan

  • Nova Tri Romadloni STMIK Nusa Mandiri Jakarta
  • Hilman F Pardede STMIK Nusa Mandiri
Keywords: Pearson Correlation, Logistic Regression, Naïve Bayes, Support Vector Machine, Opinion Mining


The comments contained on e-commerce users generally contain opinions about positive or negative experiences at several online shops. Sentences that can be written indirectly both a little or a lot, will affect other potential customers. So as a result of these comments cause a product sold at an online store has a rating of two things namely "recommended" or "non-recommended". However, detection of positive and negative opinions manually will require more time because of the large amount of data. For this reason opinion mining using technology in data mining can be used to automate positive and negative detection of comments. However, one of the main problems in opinion mining is limited data but has a large number of attributes. In this study, we propose the application of Pearson correlation (PC) based feature selection for opinion mining optimization. The results of the experiment show that the application of PC increases the performance of opinion mining systems in 3 types of classification, namely Logistic Regression, Naïve Bayes and Support Vector Machine, resulting in more optimal accuracy, namely 98.80%, 87.87% and 98.12%.


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Widiyanto, I., & Prasilowati, S. L. (2015). Perilaku Pembelian Melalui Internet. Jurnal Manajemen Dan Kewirausahaan (Journal of Management and Entrepreneurship), 17(2), 109–112.

Agustina, L., & Fayardi, A. O. (2019). Online Review : Indikator Penilaian Kredibilitas Online dalam Platform E-commerce. (4), 141–154.

Kusumasondjaja, S., Shanka, T., & Marchegiani, C. (2012). Journal of Vacation Marketing.

C, A. R., Lukito, Y., Informatika, P. T., Informasi, F. T., Kristen, U., & Wacana, D. (2017). Deteksi Komentar Spam Bahasa Indonesia Pada Instagram Menggunakan Naive Bayes. IX(1).

Asghar, M. Z., Kundi, F. M., Khan, A., & Ahmad, S. (2014). Lexicon-Based Sentiment Analysis in the Social Web. J. Basic. Appl. Sci. Res.

Zubrinic, K., SJEKAVICA, T., MILICEVIC, M., & OBRADOVIC, I. (2018). A Comparison of Machine Learning Algorithms in Opinion Polarity Classification of Customer Reviews. International Journal of Computers, 3, 159–163.

Wen, H., & Zhao, J. (2017). Aspect term extraction of E-commerce comments based on model ensemble. 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017, 2018-February,24–27.

Purwanto, D. D., & Santoso, J. (2015). Multinomial Naïve Bayes Classifier Untuk Menentukan Review. (March), 117–122. Retrieved from

Rozy, F., Rangkuti, S., Fauzi, M. A., Sari, Y. A., Dewi, E., & Sari, L. (2018). Analisis Sentimen Opini Film Menggunakan Metode Naïve Bayes dengan Ensemble Feature dan Seleksi Fitur Pearson Correlation Coefficient. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(12), 6354–6361.

Sharma, A., & Dey, S. (2012). Performance Investigation of Feature Selection Methods and Sentiment Lexicons for Sentiment Analysis. International Journal of Computer Applications, (June), 15–20. Retrieved from

Sugiyono. (2013). Metode Penelitian Pendidikan Pendekatan Kuantitatif, Kualitatif, dan R&D. Bandung: Alfabeta.

Shardlow, M. (2016). An Analysis of Feature Selection Techniques. The University of Manchester, (1), 1–7. Retrieved from

How to Cite
Romadloni, N. T., & Hilman F Pardede. (2019). Seleksi Fitur Berbasis Pearson Correlation Untuk Optimasi Opinion Mining Review Pelanggan . Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 505 - 510.
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