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.