Iqbal, Abdul Rahman and Miftahuddin, Yusup (2022) Implementasi SVM Untuk Deteksi Komentar Negatif Berbahasa Indonesia di Twitter. In: Prosiding Diseminasi Fakultas Teknik Industri 2021/2022, Bandung.

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Abstract

The development of social networks is increasing rapidly, because the role of technology cannot but update in each person's life. Different backgrounds of social media users can lead to differences in the way you communicate, express different opinions and points of view, whether those opinions are positive or negative. pole. The step taken in this search is the OCR process that identifies the text and numbers as images that originally appeared as a text file. In addition, the word processor preprocessing includes encoding, folding, stopping word deletion, and rooting. Next, object selection is to get the objects in each word to use as the classification parameter. To decide if comments contain positive or negative connotations using the SVM kernel. The data is taken from the social network Twitter with a total of 254 comment data. Based on the results of the experiments performed, the kernel of the radius basis function (RBF) with gamma = 0.05, cost = 10 gives 88% accuracy, 100% precision, 50% recall and f1-Score is 67%. Keywords: SVM, OCR, Social Media, Negative Comment, Classification

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