Implementation of a Province Clustering Dashboard Based on IPM and Smoking Prevalence 2024 Using K-Means with KNN Validation as a Support for Data-Driven Decision Making
DOI:
https://doi.org/10.51601/ijse.v6i2.649Abstract
Differences in the Human Development Index (HDI) and smoking rates across provinces in Indonesia indicate variations in social conditions that need to be systematically analyzed. This study aims to implement an interactive dashboard to cluster provinces based on their HDI and smoking rates for the year 2024 using the K-Means algorithm, and to validate the clustering results using K-Nearest Neighbor (KNN). The research data were obtained from the Central Statistics Agency (BPS) and the Regional Management Information System (SIMREG-Bappenas) and processed using RapidMiner Studio. The research stages included data cleaning, normalization using Z-transformation, clustering with K-Means into three clusters, validation using KNN via cross-validation, and the implementation of a Streamlit-based dashboard. The results show that provinces in Indonesia can be grouped into three clusters with distinct characteristics based on HDI values and smoking rates. The developed dashboard presents the analysis results in the form of tables, graphs, and interactive maps, thereby facilitating data interpretation and supporting data-driven decision-making. Validation results indicate that the clustering model exhibits a high level of consistency, making it suitable as a basis for formulating policy recommendations regarding regional development and public health.
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