Open AccessComparison of Text Mining Classification Algorithms in Interbank Money Transfer Application
Author(s) -
Siti Masripah,
Lila Dini Utami,
Hilda Amalia,
Dini Nurlaela,
Muhamad Ryansyah,
Lestari Yusuf
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1641/1/012088
Subject(s) - communication source , naive bayes classifier , algorithm , computer science , k nearest neighbors algorithm , database transaction , value (mathematics) , statistical classification , machine learning , transfer (computing) , bayes' theorem , artificial intelligence , data mining , support vector machine , computer network , bayesian probability , database , parallel computing
Funds transfer is a series of orders from the sender whose purpose is to move money from the sender to the recipient. The high interbank transaction fees imposed on each bank makes people use an interbank money transfer application, interbank money transfer transactions such as the Flip application are much in demand by the public because there are no administrative fees imposed on users. Opinion of the users of the application is processed using a text mining classification algorithm, namely the Naïve Bayes Algorithm and k-NN, the two algorithms are compared to produce which algorithm has high accuracy in processing the opinion of the flip money transfer application. Based on this matter, researchers conducted a sentiment analysis of the Flip Application, K-Nearest Neighbor (k-NN). After conducting research on sentiment analysis of Flip Applications, the Naïve Bayes classification algorithm has an accuracy of 91.25% and an ROC curve with an AUC value of 0.500. Whereas K-Nearest Neighbor has an accuracy of 85.25% and an ROC curve with an AUC value of 0.937. The Naïve Bayes algorithm can be said to be ”good classification” and the public can make the decision to use the Flip Application.