Particle Swarm Optimization for Optimizing Public Service Satisfaction Level Classification

Abstract

This research aims to categorize survey data to determine the level of satisfaction with the services provided by the village government as a public service provider. Villages or sub-districts currently offer services in response to community demand, although only partially or as efficiently as possible. The data collection technique used was distributing questionnaires to the village community. The method used for classification is the machine learning method. Before the classification process, feature selection is carried out at the data pre-processing stage using Particle Swarm Optimization (PSO), which has been proven to increase the accuracy of the classification values. The classification methods employed include Decision Tree (DT), Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms for classification purposes. This study achieves the maximum level of accuracy in decision tree classification, attaining an accuracy rate of 97.74%. Subsequently, the KNN algorithm achieved an accuracy of 77.90%, the Nave Bayes algorithm achieved 64.4%, and the SVM algorithm, which yielded the lowest accuracy value, achieved 59.90%. Following the application of Particle Swarm Optimization (PSO) for optimization, the accuracy of the SVM and KNN algorithms improved to 98.3%. The Decision Tree algorithm achieved a value of 97.77%, while the Naive Bayes technique yielded a value of 69.30%.