The Effects of Data Sampling and Feature Selection on Public Service Satisfaction Using an Ensemble Classifier Algorithm - Priatna, Wowon (The Indonesian Journal of Computer Science, 2025)
Customer satisfaction is an important factor that determines quality. User satisfaction analysis can identify the service quality and measure quality through an evaluation process to improve services. This research aims to measure the performance of services provided by the village government. Villages and sub-districts offer services based on the community's specific needs. Nevertheless, by delivering impeccable service, it is possible to satisfy the community without causing physical or material harm. An essential requirement is the development of a service user classification methodology to enhance service quality, efficiently address service user grievances, detect recurring trends, and promptly offer feedback to enhance the offerings of products and services. Machine learning approaches can be used to quantify public service satisfaction in the analytical process. Machine learning is an algorithmic approach used to assess and prioritize satisfaction with public services offered by service providers. The main approach for machine learning is an ensemble classifier. The data was analyzed using Excel; then, the data was processed first to create a classification model. At the preprocessing stage, the data is grouped to obtain labels/targets to be processed based on algorithmic classification. The classification uses the Classifier aggregation algorithm. Type improvements using optimization features using the Particle Swarm Optimization (PSO) sampling algorithm and random subsampling techniques. This research produced an accuracy value before adding sampling techniques and a PSO accuracy value of 92.68. After adding sampling techniques and PSO optimization, an accuracy value of 100% was obtained