Text Mining Algorithm Naive Bayes Classifier to Improve Quality Sentiment Analysis Passport Mobile Application
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Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute
Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute

Journal of Social and Political

Sciences

ISSN 2615-3718 (Online)

ISSN 2621-5675 (Print)

asia insitute of research, journal of social and political sciences, jsp, aior, journal publication, humanities journal, social journa
asia insitute of research, journal of social and political sciences, jsp, aior, journal publication, humanities journal, social journa
asia insitute of research, journal of social and political sciences, jsp, aior, journal publication, humanities journal, social journa
asia insitute of research, journal of social and political sciences, jsp, aior, journal publication, humanities journal, social journa
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doi
open access

Published: 04 March 2024

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

Wilonotomo, Budy Mulyawan, M. Ryanindityo, Muhammad Alvi Syahrin, Feni Yuli Triana

Immigration Polytechnic (Indonesia), Directorate General of Immigration (Indonesia)

journal of social and political sciences
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doi

10.31014/aior.1991.07.01.475

Pages: 169-178

Keywords: Text Mining, Algorithm, Naive Bayes Classifier, Sentiment Analysis, KDD, Mobile Passport

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.

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