Identifying Financial Literacy and Asset Participation Segments among Young Adults in Indonesia Using K-Means Clustering
DOI:
https://doi.org/10.70609/g-tech.v10i3.10684Keywords:
Financial Literacy, Asset Participation, Exploratory Data Analysis, K-Means, ClusteringAbstract
Financial literacy is crucial in influencing asset ownership decisions among young adults; yet, the variability of financial literacy and asset involvement in Indonesia has not been adequately examined. This research seeks to categorize young Indonesian individuals based on financial literacy and asset participation using the K-Means clustering technique. The research employed a quantitative methodology, incorporating exploratory data analysis of a survey dataset comprising 952 participants and 13 variables related to financial literacy, asset involvement, demographic traits, economic education, and financial behavior. Missing values were addressed by group-based mode imputation for categorical variables and mean imputation for numerical variables, followed by encoding and data standardization utilizing StandardScaler. The ideal number of clusters was assessed by the Elbow Method, Silhouette Score, and Davies–Bouldin Index. Despite achieving the highest Silhouette Score at k = 2, the k = 9 model was chosen due to its lower Davies–Bouldin Index and its ability to enable more nuanced responder segmentation. The findings identified nine categories exhibiting varying levels of basic and advanced financial literacy, ranging from very low to very high. These findings offer significant insights for the formulation of targeted financial education initiatives and financial inclusion policies customized to the attributes of various young adult demographics.
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Copyright (c) 2026 Oktavia Citra Resmi Rachmawati, Kevin Ilham Apriandy, Kevin Harlis Oktaviano, Zakha Maisat Eka Darmawan
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