Monitoring The Quality of Getcontact Reviews Using Integration Sentiment Analysis and Statistical Process Control

Abstract

The protection of digital communications becomes important as cybercrime rises. Getcontact, a digital communication app that offers services ranging from caller identification to spam detection, has garnered a range of opinions on Google Play Store, which serve as a basis for evaluating service quality. This study aims to monitor Getcontact’s service quality through integration of sentiment analysis and control charts based on reviews. The research data consists of reviews obtained through web scraping during the period from January 1, 2023, to September 30, 2025. The data was classified using SVM and then analyzed using p-control charts and Laney p-control charts. The results show that the negative category dominates the reviews data. SVM achieved excellent performance with accuracy 98%, precision 98%, specificity 98%, sensitivity 97%, and F1-score 97%. Control charts indicate that the process is not yet fully under control, with the Laney p chart being more representative. Pareto chart shows that complaints are dominated by issues in number identification accuracy. It is concluded that Getcontact’s service quality still needs improvement. Integration of sentiment analysis and control charts is effective for continuous quality monitoring, with the Laney p chart being more suitable.