Analisis Sentimen pada Opini Pengguna Aplikasi PLN Mobile untuk Meningkatkan Kualitas Layanan PLN Mobile

Abstract

PLN Mobile application is launched to enhance user satisfaction with the services provided. The application usage has increased since the Covid-19 pandemic and continues to be widely used. To assess the quality of the PLN Mobile Application, a sentiment analysis is conducted to enhance PLN Mobile service quality. The Naïve Bayes algorithm and RapidMiner Software are used for this purpose. The process begins with data crawling, followed by various tasks such as cleaning, tokenization, transformation, cases, and stopwords removal. Subsequently, the data is filtered. The Naïve Bayes model is then done and tested to get the value of accuracy. A total of 1068 data are processed, divided into 320 data training and 748 data testing. The accuracy of the Naïve Bayes model achieves 70.28%.