Indonesian Gender Equality Survey Analysis Using Feature Selection Based Clustering

Takako Hashimoto, Kilho Shin, David Lawrence Shepard, Tetsuji Kuboyama

Abstract: This paper presents an analysis of an Indonesian gender equality survey: in 2019, we conducted a survey of attitudes about gender roles in Indonesia and obtained data from 122 individuals. The obtained data were analyzed using our original clustering method (UFVS, Unsupervised Feature Value Selection) to form clusters. The extracted features characterized the clusters and helped to analyze the attitudes of Indonesians towards gender equality. This method allowed the respondents to be grouped by features and each group characteristics could be easily identified. It facilitated the understanding of the survey data.