ANALISIS SENTIMEN ULASAN BANTUAN SOSIAL (BANSOS) DI TWITTER MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM)

GILANG  BRILIAN RACHMAT; Puji  Winar Cahyo; Fajar Syahruddin

doi:10.30989/teknomatika.v15i1.1137

GILANG BRILIAN RACHMAT Universitas Jenderal Achmad Yani Yogyakarta
Puji Winar Cahyo
Fajar Syahruddin Universitas Jenderal Achmad Yani Yogyakarta

DOI: https://doi.org/10.30989/teknomatika.v15i1.1137

Abstract

Background: Social assistance (bansos) is assistance provided to the community/social institutions in a non-continuous and selective manner in the form of money/goods to the community, aiming to improve the welfare of the community. The purpose of this study is to create an analytical model using the Support Vector Machine method which is used to perform Sentiment analysis regarding social assistance (bansos) on Twitter. Research Method: using the Support Vector Machine (svm) method. Based on the classification results, a lot of negative tweet data and many netizens regret that social assistance is still not evenly distributed and there is still a lot of social assistance corruption by the government itself which is marked by a lot of negative sentiments rather than positive sentiments. Conclusion: This study succeeded in testing the accuracy using the Support Vector Machine (SVM) method with a value of 84% on training data and 97% on testing data.

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