Penerapan SVM untuk Klasifikasi Sentimen pada Review Comment Berbahasa Indonesia di Online Shop

Yoshua Refo, Silvia Rostianingsih, Liliana Liliana

Abstract


With so many users accessing online shops, comments are an important aspect when shopping. Buyers can provide comments about the goods or thing that have been purchased, both negative comments and positive comments. By collecting various kinds of comments, the data can be used to classify comments. This research will use the Support Vector Machine (SVM) algorithm which is considered the right method for text classification. The method will be tested for its performance, seen from how good and accurate the method used in classifying comments is. In addition, this research also uses kernels, namely Linear kernels, Radial Basis Function (RBF) kernels, and Polynomial kernels as test scenarios. Based on the test results shown, SVM is a good method in classifying text. SVM classifies text that has gone through the preprocessing stage with an accuracy value of 88% on the RBF kernel, 87% on the linear kernel, and 87% on the polynomial kernel. The accuracy value in the aspect classification itself is 78% on the RBF kernel, 78% on the Linear kernel and 74% on the Polynomial kernel.

Keywords


Text Classification; Positive Comment; Negative Comment; Support Vector Machine; Aspect

Full Text:

PDF

References


Al Amrani, Y., Lazaar, M. and El Kadiri, K., 2018. Random

Forest and Support Vector Machine based Hybrid Approach

to Sentiment Analysis. Procedia Computer Science, 127,

pp.511-520.

Betancourt, R. and Chen, S., 2019. pandas Library. Python

for SAS Users, [online] pp.65-109. URI=

https://www.researchgate.net/publication/335669917_panda

s_Library [Accessed 7 July 2022].

Bird, S., Klein, E. and Loper, E., 2009. Natural language

processing with Python. 1st ed. Sebastopol: O'reilly, pp.211-

Hermanto, Kuntoro, A., Asra, T., Pratama, E., Effendi, L.,

& Ocanitra, R. 2020. Gojek and Grab User Sentiment

Analysis on Google Play Using Naive Bayes Algorithm

And Support Vector Machine Based Smote

Technique. Journal Of Physics: Conference Series, 1641(1),

DOI= 10.1088/1742-6596/1641/1/012102

Kiilu, K., Okeyo, G., Rimiru, R., & Ogada, K. (2018).

Using Naïve Bayes Algorithm in detection of Hate Tweets.

International Journal Of Scientific And Research

Publications (IJSRP), 8(3), 99-107. DOI=

29322/ijsrp.8.3.2018.p7517

Luque, A., Carrasco, A., Martín, A. and de las Heras, A.,

The impact of class imbalance in classification

performance metrics based on the binary confusion

matrix. Pattern Recognition, 91, pp.216-231.

Nelli, F., n.d. Python data analytics. 1st ed. Apress, pp.237-

Ofoeda, J., Boateng, R. and Effah, J., 2019. Application

Programming Interface (API) Research. International

Journal of Enterprise Information Systems, 15(3), pp.76-95.

Rohmawati, U., Sihwi, S., & Cahyani, D. (2018). SEMAR:

An Interface for Indonesian Hate Speech Detection Using

Machine Learning. 2018 International Seminar On

Research Of Information Technology And Intelligent

Systems (ISRITI). DOI= 10.1109/isriti.2018.8864484

Syahputra, H. (2021). Sentiment Analysis of Community

Opinion on Online Store in Indonesia on Twitter using

Support Vector Machine Algorithm (SVM). Journal Of

Physics: Conference Series, 1819(1), 012030. DOI=

1088/1742-6596/1819/1/012030

Wong, T., 2015. Performance evaluation of classification

algorithms by k-fold and leave-one-out cross

validation. Pattern Recognition, 48(9), pp.2839-2846.


Refbacks

  • There are currently no refbacks.


Jurnal telah terindeks oleh :