%A NIM.: 16650044 Muhammad Sholihuddin Nur
%O Pembimbing :  Muhammad Didik Rohmad Wahyudi, S.T., MT.
%T ANALISIS SENTIMEN TWITTER TERHADAP PROFESI TUKANG PARKIR MENGGUNAKAN METODE NAÏVE BAYES
%X The existence of the parking attendant profession is a topic that can be quite
energy-consuming when discussed, both in terms of functionality, value and price
of services provided, and also the urgency of its existence, because there is no
research that discusses it. Therefore, a study of sentiment analysts on the parking
attendant profession is carried out.
This study classifies 1357 data taken from Twitter to be analyzed by the Naïve
Bayes method with TF-IDF as a term weighting model, and uses a split validation
and confusion matrix as the evaluation stage of the model, then the next
implementation of 1000 new data to predict.
The split validation ratio used in each model shows different accuracy using the
evaluation of the confusion matrix. Model with a split ratio of 90:10 producing an
accuracy of 82.35%, a split ratio of 80:20 producing an accuracy of 79.04%, while
a split ratio of 70:30 resulting in an accuracy of 78.92%. The results of the
implementation to the test data of 1000 tweets taken in the span of July 7-12 using
the model produces positif tweet predictions of 28.9% and negatif tweet predictions
of 71.1%.
%K Analisis Sentimen, Twitter, Metode Native Bayes
%D 2020
%I UIN SUNAN KALIJAGA YOGYAKARTA
%L digilib42669