Machine learning classification analysis model community satisfaction with traditional market facilities as public service

Abstract

Traditional markets are public service facilities that can be utilized by the community. The market function is used place where sellers and buyers meet in conducting transactions. This study aims to build a machine learning classification analysis model in measuring community satisfaction with traditional market facilities. The analytical methods used include Fuzzy. multiple linear regression (MRL), artificial neural network (ANN), and decision tree (DT). Fuzzy is used to generate a pattern of rules in determining the level of satisfaction. MRL serves to measure and test the correlation of rules that have been formed. The ANN method is used to carry out the classification analysis process based on learning. In the final stage. DT is used to describe the decision tree of the analysis process. This study presents the results of machine learning analysis which is very good in determining satisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzy logic which can develop a classification rule pattern of 32 patterns. MRL also shows a significant correlation level of 81.1% based on the indicator variables. Overall, the machine learning classification analysis model can provide knowledge to be considered in the management of traditional markets as public service facilities.