References
[1]. Aljahdali, S., & Hussain, S. N. (2013). Comparative
prediction performance with support vector machine and
random forest classification techniques. International
Journal of Computer Applications, 69(11), 12–16. https://
doi.org/10.5120/11885-7922.
[2]. Al-Mejibli, I. S., Alwan, J. K., & Abd Dhafar, H. (2020).
The effect of gamma value on support vector machine
performance with different kernels. International Journal of
Electrical and Computer Engineering, 10(5), 5497–5506.
https://doi.org/10.11591/IJECE.V10I5.PP5497-5506
[3]. Breiman, L. (2001). Random forests. Machine Learning,
45(1), 5-32. https://doi.org/10.1023/A:1010933404324
[4]. Bunker, R., & Susnjak, T. (2019). The application of
machine learning techniques for predicting results in team
sport: a review. Cornell University. https://doi.org/10.13140/
RG.2.2.22427.62245
[5]. Czarnecki, W. M., Podlewska, S., & Bojarski, A. J. (2015).
Robust optimization of SVM hyperparameters in the
classification of bioactive compounds. Journal of
Cheminformatics, 7(1), 1-15. https://doi.org/10.1186/s133
21-015-0088-0
[6]. Guia, M., Silva, R. R., & Bernardino, J. (2019).
Comparison of Naïve Bayes, Support Vector Machine,
Decision Trees and Random Forest on Sentiment Analysis.
11th International Conference on Knowledge Discovery
and Information Retrieval (KDIR), (pp. 525-531). https://doi.
org/10.5220/0008364105250531
[7]. Huang, M. W., Chen, C. W., Lin, W. C., Ke, S. W., & Tsai,
C. F. (2017). SVM and SVM ensembles in breast cancer
prediction. PloS One, 12(1), 1–14. https://doi.org/10.1371/
journal.pone.0161501
[8]. Karthika, P., Murugeswari, R., & Manoranjithem, R.
(2019, April). Sentiment analysis of social media network
using random forest algorithm. In 2019, IEEE International
Conference on Intelligent Techniques in Control,
Optimization and Signal Processing (INCOS) (pp. 1-5). IEEE.
https://doi.org/10.1109/INCOS45849.2019.8951367
[9]. Madalgi, J. B., & Kumar, S. A. (2018, December).
Development of wireless sensor network congestion
detection classifier using support vector machine. In 2018,
3rd International Conference on Computational Systems
and Information Technology for Sustainable Solutions
(CSITSS) (pp. 187-192). IEEE. https://doi.org/10.1109/CSITSS.
2018.8768738
[10]. Martins, S., Bernardo, N., Ogashawara, I., &
Alcantara, E. (2016). Support vector machine algorithm
optimal parameterization for change detection mapping
in Funil Hydroelectric Reservoir (Rio de Janeiro State, Brazil).
Modeling Earth Systems and Environment, 2(3), 1-10.
https://doi.org/10.1007/s40808-016-0190-y
[11]. Ong, C. J., Orfanoudaki, A., Zhang, R., Caprasse, F. P.
M., Hutch, M., Ma, L., ... & Bertsimas, D. (2020). Machine
learning and natural language processing methods to
identify ischemic stroke, acuity and location from radiology
reports. PloS One, 15(6). https://doi.org/10.1371/journal.
pone.0234908
[12]. Panguila, K. F. M., & Chandra, J. (2019). Sentiment
analysis on social media data using intelligent techniques.
International Journal of Engineering Research and
Technology, 12(3), 440-445.
[13]. Paquin, F., Rivnay, J., Salleo, A., Stingelin, N., & Silva,
C. (2013). Multi-phase semicrystalline microstructures drive
exciton dissociation in neat plastic semiconductors.
Journal of Materials Chemistry C, 3(2), 10715–10722.
https://doi.org/10.1039/b000000x
[14]. Prasad, S. V. S., Savithri, T. S., & Krishna, I. V. M. (2017).
Performance evaluation of SVM kernels on multispectral
LISS III data for object classification. International Journal
on Smart Sensing and Intelligent Systems, 10(4), 829-844.
https://doi.org/10.21307/ijssis-2018-020
[15]. Rustam, F., Khalid, M., Aslam, W., Rupapara, V.,
Mehmood, A., & Choi, G. S. (2021). A performance
comparison of supervised machine learning models for
Covid-19 tweets sentiment analysis. Plos One, 16(2). https://
doi.org/10.1371/journal.pone.0245909.
[16]. Saifullah, S., Fauziah, Y., & Aribowo, A. S. (2021).
Comparison of machine learning for sentiment analysis in
detecting anxiety based on social media data. Cornell
University, 15(1), 45-55. https://doi.org/10.26555/jifo.v15i1.
a20111
[17]. Sain, S. R. (1996). The nature of statistical learning
theory. Technometrics, 38(4), pp. 409. https://doi.org/10.1
080/00401706.1996.10484565
[18]. Tarigan, A., Agushinta, D., Suhendra, A., & Budiman,
F. (2017). Determination of SVM-RBF Kernel Space
Parameter to Optimize Accuracy Value of Indonesian Batik
Images Classification. Journal of Computer Science,
13(11), 590-599. https://doi.org/10.3844/jcssp.2017.590.
599
[19]. Tedmori, S., & Awajan, A. (2019). Sentiment analysis
main tasks and applications: a survey. Journal of
Information Processing Systems, 15(3), 500-519.
https://doi. org/10.3745/JIPS.04.0120
[20]. Valerio, R., & Vilalta, R. (2014, June). A data
complexity approach to kernel selection for support vector
machines. In Proceedings of the AAAI Conference on
Artificial Intelligence, 28(1), 3138–3139.
[21]. Wadhe, A. A., & Suratkar, S. S. (2020, February). Tourist
Place Reviews Sentiment Classification Using Machine
Learning Techniques. In 2020, International Conference on
Industry 4.0 Technology (I4Tech) (pp. 1-6). IEEE. https://doi.
org/10.1109/I4Tech48345.2020.9102673
[22]. Willianto, T., & Wibowo, A. (2020). Sentiment analysis
on E-commerce product using machine learning and
combination of TF-IDF and backward elimination.
International Journal of Electrical and Computer
Engineering (IJECE), 8(6), 2862-2867. https://doi.org/10.35
940/ijrte.f7889.038620
[23]. Zhang, D., Xu, H., Su, Z., & Xu, Y. (2015). Chinese
comments sentiment classification based on word2vec
and SVMperf. Expert Systems with Applications, 42(4),
1857-1863. https://doi.org/10.1016/j.eswa.2014.09.011
