Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier

Abstract

The city of Medan is one of the largest cities in Indonesia which has tourist charm, culinary and also supporting facilities and infrastructure that are quite adequate in the city, and of course will be ogled by foreign or foreign tourism. So it is necessary to do research in order to find out how the public sentiment towards tourism in the city of Medan, whether the majority of the world community considers it positive or negative. The response of the world community regarding Medan tourism is obtained from the Application Programming Interface (API) on Twitter because social media has a lot of users in the world. Indonesia has even reached 19.5 million users out of a total of 300 million users worldwide. In this study, one part of the Text Mining algorithm is text preprocessing, the text preprocessing used is case folding, tokenizing, stopwords, and stemming. For the preprocessing stemming, the snowball stemmer algorithm is used, while the analysis of the text data classification uses the Naïve Bayes Classifier algorithm and to partition the data the K-Fold Cross Validation method will be used. The data used in this study is a collection of tweets about tourism in the city of Medan on December 1, 2019 until December 8, 2019. Data is obtained from the Twitter API (Application Programming Interface) as many as 2000 tweets. Extracting data from Twitter via Twitter API (Application Programming Interface) using RStudio as a console for the data retrieval process