Sentiment Analysis of the Digital Population Identity Application Reviews Using the Support Vector Machine Method
Abstract
Digital Population Identity Application, developed by the Directorate General of Civil Registration of the Ministry of Home Affairs, Indonesia, represents a digitalization initiative for population documents including electronic ID cards (KTP-el), Family Cards, Covid-19 vaccination certificates, tax identification numbers (NPWP), vehicle ownership information, National Civil Service Agency (BKN) data, social security (BPJS), national socioeconomic data (DTKS), and voter lists. Sentiment analysis is crucial to understand user feedback on the application. This study aims to analyze user sentiment toward the Digital Population Identity Application on Google Play Store, categorize sentiments using ISO 9126 standards, and evaluate accuracy using Support Vector Machine (SVM) algorithms within the framework of the Cross-Industry Standard Process for Data Mining (CRISP-DM). Research findings indicate positive sentiment from users toward the Digital Population Identity Application, with a primary focus on application functionality in positive reviews. SVM models trained using lexicon-based labeling achieved an accuracy of 80%, while models trained with ISO 9126 labeling achieved 84% accuracy. The conclusion of this study is that the Digital Population Identity Application is well-received by users, providing valuable guidance for developers to improve the quality and future development of the application.
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