Application of K-Means on Human Rights, Demographic,  Economic, and Crypto Investment Data

Akianus Wenda; Antonius R Kopong Notan; Shalwa Azizah Rananda Sudirman; T. Ferdiansyah; Tegar Surya Pratama; Zurnan Alfian

doi:10.59934/jaiea.v4i3.1215

Authors

Akianus Wenda Pamulang University
Antonius R Kopong Notan Pamulang University
Shalwa Azizah Rananda Sudirman Pamulang University
T. Ferdiansyah Pamulang University
Tegar Surya Pratama Pamulang University
Zurnan Alfian Pamulang University

DOI:

https://doi.org/10.59934/jaiea.v4i3.1215

Keywords:

Data Mining, K-Means, Decision Tree, ESG, Human Rights, Sustainable Finance

Abstract

This study combines the K-Means Clustering and Decision Tree methods to analyze multidomain data covering economic and social human rights, demographics, poverty, crypto investment, and sustainable financing in Indonesia's financial services sector. Data was obtained from various credible sources such as the National Commission on Human Rights (Komnas HAM), the Central Statistics Agency (BPS), the Financial Services Authority (OJK), and scientific publications (2019–2023), then processed through missing value handling, outlier detection, and normalization using Min-Max Scaling and Z-score. K-Means was used to group regions based on the similarity of socio-economic and financial indicators, while Decision Tree was used to classify financial entities based on ESG (Environmental, Social, and Governance) scores. Model evaluation was conducted using WCSS, Silhouette Score, Davies-Bouldin Index, and classification accuracy. The results show the formation of clusters representing different levels of inequality and sustainability in Indonesia. This approach contributes to understanding the dynamics of multidimensional development and provides a basis for more adaptive and sustainable policies in the socio-economic and financial sectors.

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