TEXT MINING WITH LATENT DIRICHLET ALLOCATION FOR ANALYZING PUBLIC COMMENTS ON THE M-PASSPORT APPLICATION

Theresia Shinta Hapsari; Yessica Nataliani

doi:10.52436/1.jutif.2024.5.4.1929

Theresia Shinta Hapsari Department of Information Systems, Faculty of Information Technology, Universitas Kristen Satya Wacana, Indonesia
Yessica Nataliani Department of Information Systems, Faculty of Information Technology, Universitas Kristen Satya Wacana, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.4.1929

Keywords: Comments, Latent Dirichlet Allocation, Text mining, Topic modeling

Abstract

The M-Passport application is a service application developed by the Directorate General of Immigration of Indonesia to assist the public in applying for new passports and replacing passports online. However, in its implementation, this application has not been able to give satisfaction to its users. It is proven by the low rating of the application and the numerous negative comments on the Google Play Store. One way to identify the application's shortcomings is by analyzing user comments. In analyzing the abundance of comment data, this study utilizes the text mining method with Latent Dirichlet Allocation (LDA) topic modeling. The analysis with this method aims to find topics frequently discussed in comments so that the government can identify the shortcomings of the M-Passport application. The results of comment analysis with LDA topic modeling produced seven topics, from which three topics with the highest coherence values were selected. These three topics are then interpreted to obtain information about the public's concerns regarding the M-Passport application. The results of this interpretation include users frequently failing to log in or register to the M-Passport application, users feeling that the M-Passport application does not assist them in passport management due to constraints in the online queue feature, and some users still finding it difficult to use the M-Passport application.

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