Sistem Analisis Sentimen Ulasan Aplikasi Belanja Online Menggunakan Metode Ensemble Learning

Debby Erce Sondakh, S.Kom, M.T, Ph.D; Semmy W.  Taju; Michelle G. Tene; Arwin E. T.  Pangaila

doi:10.31154/cogito.v9i2.525.280-291

Authors

Debby Erce Sondakh, S.Kom, M.T, Ph.D Universitas Klabat
Semmy W. Taju Fakultas Ilmu Komputer, Universitas Klabat
Michelle G. Tene Fakultas Ilmu Komputer, Universitas Klabat
Arwin E. T. Pangaila Fakultas Ilmu Komputer, Universitas Klabat

DOI:

https://doi.org/10.31154/cogito.v9i2.525.280-291

Keywords:

Sentiment Analysis, Ensemble Learning, Prediction, Majority Voting, Online Shopping

Abstract

There is a growing number and prevalence of online shopping. Online shopping technology allows shoppers to provide post-purchase feedback (comments and reviews) regarding the app itself and other aspects of the product. This feedback can be beneficial to both customers and businesses. However, manually sorting, categorising and reading so many reviews is time-consuming. Sentiment analysis can investigate customer behaviour, opinions, and emotions through text comments/reviews. In this research, a Sentiment Analysis System was developed to help determine the sentiment of each review by displaying attractive visualisations of the analysis results through pie charts, word occurrence frequency, and percentage probability of each sentiment class-positive, neutral, and negative. The Sentiment Analysis System uses an ensemble learning classifier model with SVM, KNN, and Random Forest algorithms. Ensemble learning produces more precise results than a single algorithm. Ensemble learning produces a classifier model with better performance, with accuracy indicators of 81.8% precision 83%, recall 82%, F1-score 82%.

Author Biography

Debby Erce Sondakh, S.Kom, M.T, Ph.D, Universitas Klabat

Reviewer

References

S. Syuhendra and A. U. Hamdani, ‘Penjualan Online Berbasis E-commerce Pada Toko ADHIZZSHOP dengan Menggunakan Woocomerce’, IDEALIS : InDonEsiA journaL Information System, vol. 3, no. 1, pp. 26–33, Jan. 2020, doi: 10.36080/idealis.v3i1.1476.

N.-W. Hanadian, ‘Number of online shoppers in Indonesia in 2017 and 2022’, Statista, Apr. 20, 2020. https://www.statista.com/statistics/971411/indonesia-number-online-shoppers/ (accessed Feb. 23, 2023).

Directorate for Science Technology and Innovation Committee on Consumer, ‘Understanding online consumer ratings and reviews’, 2019.

M. Birjali, M. Kasri, and A. Beni-hssane, ‘A comprehensive survey on sentiment analysis : Approaches, challenges and trends’, Knowl Based Syst, vol. 226, p. 107134, 2021, doi: 10.1016/j.knosys.2021.107134.

P. Kumar, R. Pamula, and G. Srivastava, ‘A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews’, Comput Sci Rev, vol. 41, p. 100413, 2021, doi: 10.1016/j.cosrev.2021.100413.

M. Rezwanul, A. Ali, and A. Rahman, ‘Sentiment Analysis on Twitter Data using KNN and SVM’, International Journal of Advanced Computer Science and Applications, vol. 8, no. 6, 2017, doi: 10.14569/IJACSA.2017.080603.

P. Sudhir and V. Deshakulkarni, ‘Comparative study of various approaches, applications and classifiers for sentiment analysis’, Global Transitions Proceedings, vol. 2, pp. 205–211, 2021, doi: 10.1016/j.gltp.2021.08.004.

Sondakh, Debby E., Maringka, Raissa C., Ayorbaba, Ferlein P., Mangi, Joanne S. C. B. T., dan Pungus, Stenly R., ‘Emotion Mining Review Pengguna Aplikasi Mobile Bankin BRImo Menggunakan Algoritma Decision Tree’, Jurnal Sistem Informasi dan Komputer, vol. 12, no. 03, pp. 350-355, 2023, doi: 10.32736/sisfokom.v12i3.1721

N. N. Yusof, A. Mohamed, dan S. Abdul-Rahman, ‘Reviewing classification approaches in sentiment analysis’, Communications in Computer and Information Science, vol. 545, pp. 43–53, 2015, doi: 10.1007/978-981-287-936-3_5.

A. Alrehili and K. Albalawi, ‘Sentiment Analysis of Customer Reviews Using Ensemble Method’, in 2019 International Conference on Computer and Information Sciences (ICCIS), IEEE, 2019, pp. 1–6.

H. Zhao, Z. Liu, X. Yao, and Q. Yang, ‘A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach Parts of Speech’, Inf Process Manag, vol. 58, no. 5, p. 102656, 2021, doi: 10.1016/j.ipm.2021.102656.

X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, ‘A survey on ensemble learning’, Front Comput Sci, pp. 1–18, 2019.

D. Tiwari and B. Nagpal, ‘Ensemble Methods of Sentiment Analysis: A Survey’, in 2020 7th International Conference on Computing Sustainable Global Development (INDIACom), New Delhi: IEEE, 2020, pp. 150–155. doi: 10.23919/INDIACom49435.2020.9083693.

T. Alqurashi and W. Wang, ‘Clustering ensemble method’, International Journal of Machine Learning and Cybernetics, vol. 10, no. 6, pp. 1227–1246, Jun. 2019, doi: 10.1007/s13042-0170756-7.

M. Hosni, I. Abnane, A. Idri, J. M. Carrillo de Gea, and J. L. Fernández Alemán, ‘Reviewing ensemble classification methods in breast cancer’, Computer Methods and Programs in Biomedicine, vol. 177. Elsevier Ireland Ltd, pp. 89–112, Aug. 01, 2019. doi: 10.1016/j.cmpb.2019.05.019.

S. Cui, Y. Wang, Y. Yin, T. C. E. Cheng, D. Wang, and M. Zhai, ‘A cluster-based intelligence ensemble learning method for classification problems’, Inf Sci (N Y), vol. 560, pp. 386–409, Jun. 2021, doi: 10.1016/j.ins.2021.01.061.

K. Golalipour, E. Akbari, S. S. Hamidi, M. Lee, and R. Enayatifar, ‘From clustering to clustering ensemble selection: A review’, Eng Appl Artif Intell, vol. 104, p. 104388, Sep. 2021, doi: 10.1016/j.engappai.2021.104388.

J. Zhou, Y. Gao, J. Lu, C. Yin, and H. Han, ‘An Ensemble Learning Algorithm for Machinery Fault Diagnosis Based on Convolutional Neural Network and Gradient Boosting Decision Tree’, J Phys Conf Ser, vol. 2025, no. 1, p. 012041, Sep. 2021, doi: 10.1088/1742-6596/2025/1/012041.

A. F. Kamara, E. Chen, and Z. Pan, ‘An ensemble of a boosted hybrid of deep learning models and technical analysis for forecasting stock prices’, Inf Sci (N Y), vol. 594, pp. 1–19, May 2022, doi: 10.1016/j.ins.2022.02.015.

A. Mabrouk, R. P. Díaz Redondo, A. Dahou, M. Abd Elaziz, and M. Kayed, ‘Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks’, Applied Sciences, vol. 12, no. 13, p. 6448, Jun. 2022, doi: 10.3390/app12136448.