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
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(1) * Dedy Rizaldi
Universitas Muhammadiyah Sidoarjo
Indonesia
(2) Ika Ratna Indra Astutik Universitas Muhammadiyah Sidoarjo
Indonesia
(3) Mochamad Alfan Rosid Universitas Muhammadiyah Sidoarjo
Indonesia
(*) Corresponding Author
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
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Copyright (c) 2024 Dedy Rizaldi, Ika Ratna Indra Astutik, Mochamad Alfan Rosid
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