SENTIMENT ANALYSIS OF DIGITAL POPULATION IDENTITY (IKD) APPLICATION USING THE NAÏVE BAYES ALGORITHM
Keywords:
Sentiment Analysis of Digital Population Identity (IKD) Application usersAbstract
This study aims to analyze user sentiment towards the digital identity application launched by the government. The methodology employed involves collecting user reviews and comments from various social media platforms and the official application. The data is then analyzed using Natural Language Processing (NLP) techniques to identify and categorize sentiments as positive, negative, or neutral. The Naive Bayes method will be implemented using a dataset containing user reviews about a digital identity application. The analysis results indicate that the majority of user sentiments towards the application are positive, with many users appreciating the ease of access and features that simplify administrative processes. However, there are also a number of negative sentiments indicating technical issues and concerns regarding data privacy. Neutral sentiments generally relate to suggestions for improvements and enhancements to the application. The findings of this study are expected to provide constructive feedback for application developers to improve the quality and security of the digital identity application, as well as to enhance user satisfaction in the future. The study uses Google Colab, a cloud-based service provided by Google that allows users to write and execute Python code directly from the browser. The researcher used the scraping method to collect review data from IKD applications on the Play Store.
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