Client Satisfaction Analysis for Delivery of Services with Natural Language Processing and Decision Support System

Traditional client satisfaction surveys in state universities and government agencies often suffer from inefficiencies in data collection, analysis, and visualization. Manual processing leads to delays, inaccuracies, and limited actionable insights, hindering effective decision-making. This study aims to improve client satisfaction surveys by developing a web-based application that integrates Natural Language Processing (NLP) and Decision Support Systems (DSS). The goal is to automate the collection, analysis, and visualization of feedback, enhancing data-driven decision-making and service improvement. A developmental and descriptive research design was used, with data collected from university employees and clients involved in service delivery and surveys. Stratified sampling ensured diverse representation from faculty and students. The system was developed using Agile methodologies, allowing for iterative improvements based on user feedback. NLP was applied to analyze open-ended responses, while DSS was used to generate actionable insights. The system reduced survey processing delays by automating data analysis and visualization. NLP sentiment analysis improved the accuracy of open-ended feedback. Real-time insights were provided through interactive dashboards, aligning with the Anti-Red Tape Authority’s (ARTA) goal of improving government service efficiency. The web-based application effectively solved inefficiencies in traditional survey methods by automating key processes. Integrating NLP and DSS improved data accuracy, reduced delays, and enhanced service delivery in government institutions. State universities and government agencies should adopt this approach to enhance the efficiency and transparency of client satisfaction surveys, with further research exploring its application in other government processes.