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All Journal Jurnal Teknik Informatika (JUTIF)
aristin chusnul khotimah
universitas amikom yogyakarta
Computer Science & IT

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COMPARISON NAÏVE BAYES CLASSIFIER, K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE IN THE CLASSIFICATION OF INDIVIDUAL ON TWITTER ACCOUNT aristin chusnul khotimah; Ema Utami
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 3 (2022): JUTIF Volume 3, Number 3, June 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.3.254

Abstract

In current’s digital era, people can take advantage of the ease and effectiveness of interacting with each other. The most popular online activity in Indonesia is the use of sosial media. Twitter is a social media that allows people to build communication between users and get the latest information or news. Information obtained from twitter can be processed to get the characteristics of a person using the DISC method, DISC is a behavioral model that helps every human being why someone does. To classify the tweet into the DISC method using algorithms naïve bayes classifier, k-nearest neighbor and support vector machine with the TF-IDF weighting. The results is compare the accuracy of the naïve bayes classifier algorithm has an accuracy rate of 31.5%, k-nearest neighbor has an accuracy rate of 23.8%, while the support vector machine has an accuracy rate of 28.4%.
Co-Authors Ema Utami