Classification of Meme Sentiment Analysis On Kaggle.com Sites To Use Support Vector Machine Algorithm Klasifikasi Analisis Sentimen Meme Pada Situs Kaggle.com Menggunakan Algoritma Support Vector Machine
Article Sidebar
Main Article Content
Abstract
Meme is currently one of the media that is often used to convey a message or opinion on a topic that is currently hot
in the community, and is widely discussed on social media. Apart from being a means of humor, memes are also
commonly used as a medium to convey satire, even 'ridicule' to a party. This encourages curiosity to capture and
classify memes circulating on social media, including through public data available on the Kaggle. This study aims
to classify memes into three classes of sentiment, namely positive, neutral, and negative. In this case, the researcher
uses Support Vector Machine algorithm with Radial Basis Function kernel because it can produce the highest
accuracy compared to other kernels. The dataset downloaded through the Kaggle website is in the form of memes that
have been labeled and accompanied by Optical Character Recognition (OCR) results consisting of a total of 6,992
meme data. By using Support Vector Machine algorithm, the classification results are obtained at 73.75% while using
Naïve Bayes algorithm to obtain an accuracy of 61.24%. This proves that the application of Support Vector Machine
algorithm in document classification is able to produce a fairly high accuracy when compared to the Naïve Bayes
algorithm
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
1–32, 2012.
[2] A. C. Najib, A. Irsyad, G. A. Qandi, and N. A. Rakhmawati, “Perbandingan Metode Lexicon-based dan
SVM untuk Analisis Sentimen Berbasis Ontologi pada Kampanye Pilpres Indonesia Tahun 2019 di
Twitter,” Fountain Informatics J., vol. 4, no. 2, p. 41, 2019, doi: 10.21111/fij.v4i2.3573.
[3] C. Raymond J Mooney, “Machine Learning Text Categorization,” Mach. Learn. Text Categ., pp. 1–6, 2006.
[4] R. Feldman and J. Sanger, The Text Mining Handbook. 2006. doi: 10.1017/cbo9780511546914.
[5] S. Sanjaya, S. Sanjaya, and E. A. Absar, “Pengelompokan Dokumen Menggunakan Winnowing Fingerprint
dengan Metode K-Nearest Neighbour,” J. CoreIT J. Has. Penelit. Ilmu Komput. dan Teknol. Inf., vol. 1, no.
2, pp. 50–56, 2015, [Online]. Available: http://ejournal.uin-suska.ac.id/index.php/coreit/article/view/1229
[6] B. Santosa, T. Conway, and T. Trafalis, “A hybrid knowledge based-clustering multi-class svm approach for
genes expression analysis,” Springer Optim. Its Appl., vol. 7, pp. 231–274, 2007, doi: 10.1007/978-0-387-
69319-4_15.
[7] Y. X. Chu, X. G. Liu, and C. H. Gao, “Multiscale models on time series of silicon content in blast furnace
hot metal based on Hilbert-Huang transform,” Proc. 2011 Chinese Control Decis. Conf. CCDC 2011, pp.
842–847, 2011, doi: 10.1109/CCDC.2011.5968300.
[8] M. Windarti, “Perbandingan Kinerja Algoritma Naïve Bayes Dan Bayesian Network Dalam Klasifikasi
Masa Studi Mahasiswa,” Pros. Semin. Nas. Apl. Sains Teknol., no. September, pp. 249–261, 2018.
[9] M. Nosrati, “50Ae4a1125D607.12866725.Pdf,” no. June, pp. 110–117, 2011.
[10] E. N. Arrofiqoh and Harintaka, “IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK
UNTUK KLASIFIKASI TANAMAN PADA CITRA RESOLUSI TINGGI ( The Implementation of
Convolutional Neural Network Method for Agricultural Plant Classification in High Resolution Imagery ),”
Geomatika, vol. 24, no. 2, pp. 61–68, 2018