KLASIFIKASI KOMENTAR TWITTER TENTANG CITRA DEWAN PERWAKILAN RAKYAT (DPR) MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN) DAN NAÏVE BAYES

Putri, RIZQIYAH (2019) KLASIFIKASI KOMENTAR TWITTER TENTANG CITRA DEWAN PERWAKILAN RAKYAT (DPR) MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN) DAN NAÏVE BAYES. Undergraduate Thesis thesis, Institut Telkom Purwokerto.

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Abstract

ABSTRACT This time Twitter is not only a platform to write microblogging messages, but its has been a place where people express their aspirations. In 2018 the DPR received a lot of criticism from the public, especially through the twitter platform, so that the data used in this research is the image of the people towards DPR, which will be classified into positive and negative. The data used were 600 data consisting of 500 training data and 100 testing data. The classification algorithm used in this research are KNN and Naive Bayes, where K-NN is an algorithm that adheres to the concept of many neighborhoods while Naive Bayes is an algorithm that adheres to the concepts of probability and statistics. The final result of this study is to compare the accuracy of two algorithms, and after the data normalization processes till produce the accuracy have been obtained, the results are K-NN get an accuracy 80% at k = 19 and 20 while Naive Bayes get an accuracy of 77%. In this case the K-NN algorithm performs better than Naive Bayes because accuracy calculations can be performed repeatedly with different k until the best accuracy is achieved while the accuracy of Naive Bayes can only be done once. But even though K-NN has a higher accuracy than Naive Bayes, Naive Bayes still has good performance in classification. Keyword:Accuracy, classification, DPR, K-NN, Naive bayes, twitter

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