Text Mining Algorithm Naive Bayes Classifier to Improve Quality Sentiment Analysis Passport Mobile Application

Abstract

Mobile Passport is an application that can be used as a digital service for people in Indonesia to apply for a new passport and an official online passport replacement from the Directorate General of Immigration replacing APAPO (Online Passport Service Application). User reviews of the Mobile Passport application are the output of big data generated as a result of the Internet of Things. The problem formulation in this research is how the implementation of the Naive Bayes text mining classifier algorithm can analyze the reviews contained in the Mobile Passport application as well as the accuracy, precision and recall values. This research uses the KDD (Knowledge Discovery and database) method which consists of data selection, data preprocessing, transformation, data mining, and evaluation using the R Studio tool. The resulting knowledge and information from this process is used as a useful knowledge base in decision making. The Naive Bayes classifier algorithm method in this research is used because of its reliability in handling data quickly and accurate predictions based on class probabilities, thus enabling research to obtain consistent and reliable results.