Analisis Sentimen X Terhadap Keamanan Data menggunakan Algoritma Naïve Bayes Classifier: Studi Kasus Pusat Data Nasional Sementara (PDNS)

Abstract

Data security has become a primary concern alongside rapid technological advancements. Managing and protecting data, especially on a large scale with thorough analysis, is crucial to prevent losses and safeguard the confidentiality of users' important documents. Modern technology and societal lifestyles support various studies enabling computers to understand, manipulate, and interpret human language. This research aims to analyze sentiments within application X regarding data security, particularly concerning incidents of breaches at the Temporary National Data Center (PDNS), using the Naïve Bayes Classifier algorithm. The data consists of Indonesian-language tweets collected within specific time frames using the keywords "pdns" and "pdns 2". This method is expected to provide deeper insights into public perceptions of data security context, including sentiment classifications such as positive, negative, and neutral visualized through graphs and word clouds. The findings of this study are anticipated to contribute to a better understanding of sentiment analysis on data security within application X and serve as a foundation forfurther research in related fields.