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Arridlo, Zulfan Amal
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Computer Science & IT

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Family Data Clustering Based on Welfare Levels Using Self-Organizing Maps Arridlo, Zulfan Amal; Rosita, Ai; Ningrum, Dedah
Journal of Applied Informatics and Computing Vol. 10 No. 5 (2026): October 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i5.13311

Abstract

Sub-district socio-economic data on family welfare from BKKBN is high-dimensional and multivariate. The data pattern is highly complex with a positive asymmetric (right-skewed) distribution. This study aims to implement the Self-Organizing Maps (SOM) algorithm to cluster these welfare characteristics, mathematically evaluate model performance to eliminate bias, and visualize the tabular results into a spatial digital map. A 2×2 SOM model optimized using Optuna was used to group family welfare characteristics in West Java for the 2022–2025 period. Optimization results identified the best hyperparameter combinations (learning rate 0.7074–0.9574, sigma 0.1167–0.2668, and 7,000–20,000 iterations). The evaluation demonstrated reliable performance with Quantization Error ranging from 2.4659–3.4776, Topographic Error between 0.0000–0.0647, and Silhouette Score consistently above 0.50. GeoJSON integration successfully mapped 4 welfare hierarchies, confirming the dominance of prosperous regions in the eastern, southern, and western areas.
Co-Authors ningrum, dedah - Rosita, Ai