Implementation of Fuzzy C-Means in Investor Group in the Stock Market Post-Covid-19 Pandemic

Abstract

This study aims to implement fuzzy c-means for groups of investors in the stock market post-covid-19 pandemic. The data used in this study is primary data generated by a Likert scale. Measurement of variables in primary data using the average score of each item. The sample selection of 100 investors is because it follows the central limit theory which says that the sampling distribution curve (for a sample size of 30 or more) will center on the population parameter values and will have all the properties of a normal distribution. This study uses an analytical method, namely fuzzy c-means. The results obtained in this study are the grouping of data into various types depending on the data for each parameter owned by the type of stock. The number of iterations is also very dependent on the value of the cluster center determined in the first iteration. Originality in this study is the object of research, namely post-pandemic stock market investors using a fairly reliable data grouping algorithm, namely Fuzzy C-Means, the algorithm groups data based on the characteristics of the data they have.