Mutiara S. Simanjuntak
Nurafni Damanik


Consumers who have shopped at E-Commerce will provide reviews/comments on products that have been purchased. Customer confidence in the rating is hampered due to inconsistency of answers such as reviews that have negative text with a positive rating value. For this reason, a technique is needed to adjust the rating with comments or reviews of purchased goods to make it easier for consumers when shopping to see the rating directly without reading the reviews/comments of previous buyers. purpose of this study is to classify comments and ratings and then obtain the results of the accuracy of the classification system so that the above problems can be answered.This study uses Support Vector Machine classification technique because this algorithm is better in classification’s terms. Data used are 1044 comment data and 1044 rating. Data are grouped into Good, Neutral, Less good categories using Python by Google Colab and divided into training and test data. To test capability of system, data that has been classified then analyzed using Confusion matrix. Results showed that SVM Algorithm was able to classify with an accuracy rate of 71.14%, 88% precision, and 79% recall.SVM algorithm is able to formulate training data with an accuracy of 91.3%.


How to Cite
Mutiara S. Simanjuntak, Nurafni Damanik, & Allwine. (2022). Performance Analysis Of Support Vector Machine In Identifying Comments And Ratings On E-Commerce. International Journal of Basic and Applied Science, 11(1), 37–46. https://doi.org/10.35335/ijobas.v11i1.79
[1] T. H. D. Chaffey, D. Edmundson-Bird, Digital Business and E-Commerce Management, 7th Edition. UK:
Pearson UK, 2019. [Online]. Available: https://www.pearson.com/uk/educators/higher-educationeducators/program/Chaffey-Digital-Business-and-E-Commerce-Management-7thEdition/PGM2542799.html
[2] D. Pujiwidodo, “The influence of online customer reviews and ratings on trust and purchase interest in
online marketplaces in Indonesia,” vol. III, no. 2, p. 2016, 2016.
[3] Regina Dwi Amelia, M. Michael, and R. Mulyandi, “Online Consumer Review Analysis of Purchase
Decisions in Beauty E-Commerce,” J. Indones. Sos. Teknol., vol. 2, no. 2, pp. 274–280, 2021, doi:
[4] D. Maulina and R. Sagara, “Indonesia. Sauce. Technol. , vol. 2, no. 2, pp. 274–280, 2021. [4] D. Maulina and
R. Sagara, "Classification of hoax articles using linear support vector machines with weighting term
frequency–Inverse document frequency,” J. Mantik Penusa, vol. 2, no. 1, pp. 35–40, 2018.
[5] N. M. Norwawi, “Recognition decision-making model using temporal data mining technique”.
[6] D. Darwis, E. S. Pratiwi, and A. F. O. Pasaribu, “Application of the Svm Algorithm for Sentiment Analysis
on Twitter Data of the Corruption Eradication Commission of the Republic of Indonesia,” Edutic - Sci. J.
Informatics Educ., vol. 7, no. 1, pp. 1–11, 2020, doi: 10.21107/edutic.v7i1.8779.
[7] M. P. Bach, Ž. Krstič, S. Seljan, and L. Turulja, “Text mining for big data analysis in financial sector: A
literature review,” Sustain., vol. 11, no. 5, 2019, doi: 10.3390/su11051277.
[8] A. K. Gupta, V. Singh, P. Mathur, and C. M. Travieso-Gonzalez, “Prediction of COVID-19 pandemic
measuring criteria using support vector machine, prophet and linear regression models in Indian scenario,”
J. Interdiscip. Math., vol. 24, no. 1, pp. 89–108, 2021, doi: 10.1080/09720502.2020.1833458.
[9] M. S. Simanjuntak, “Decision Support System For Admission Of New Employees With Fuzzy Madm Model
Weighted Product At Pt. Super Andalas Stell,” Univ. POTENSI UTAMA, 2020.
[10] B. Gupta, M. Negi, K. Vishwakarma, G. Rawat, and P. Badhani, “Study of Twitter Sentiment Analysis using
Machine Learning Algorithms on Python,” Int. J. Comput. Appl., vol. 165, no. 9, pp. 29–34, 2017, doi:
[11] M. S. Simanjuntak and J. Panjaitan, “Information Retrieval System Using K- Nearest Neighbour In Journal
Classification,” vol. 1, no. 2, pp. 1–8, 2021.
[12] M. S. Simanjuntak, “Jurnal Mantik Jurnal Mantik,” Act. Act. Funct. Multilayer Perceptron - Based Card.
Abnorm., vol. 3, no. 2, pp. 10–19, 2019, [Online]. Available:
[13] R. Rosnelly, D. Hartama, M. Sadikin, C. Lubis, M. Simanjuntak, and S. Kosasi, “The Similarity of Essay
Examination Results using Preprocessing Text Mining with Cosine Similarity and Nazief-Adriani
Algorithms,” Turkish J. Comput. Math. Educ., vol. 12, pp. 1415–1422, Apr. 2021