A Random Forest Approach for Classifying Regional Social Stability in Banten Province Based on Socioeconomic Indicators

Widyawati; Ika Ima Nissa; Bagus Setya; Lisdianto Dwi Kesumahadi; Yuda Pratama Wibawa

doi:10.34148/teknika.v15i2.1490

Authors

Widyawati Information Systems Study Program, Universitas Pamulang, Banten, Indonesia
Ika Ima Nissa Information Systems Study Program, Universitas Pamulang, Banten, Indonesia
Bagus Setya Information Systems Study Program, Universitas Pamulang, Banten, Indonesia
Lisdianto Dwi Kesumahadi Information Systems Study Program, Universitas Pamulang, Banten, Indonesia
Yuda Pratama Wibawa Information Systems Study Program, Universitas Pamulang, Banten, Indonesia

DOI:

https://doi.org/10.34148/teknika.v15i2.1490

Keywords:

Banten Province, Classification, Machine Learning, Random Forest, Social Stability

Abstract

Social stability is an important indicator in measuring regional welfare and development conditions. This study aims to classify the level of social stability in Banten Province using the Random Forest method based on several social indicators, including poverty, unemployment, Human Development Index (HDI), HDI percentage growth, Gini Ratio, population density, and average years of schooling (RLS). The dataset used in this study was obtained from the regency and city social statistical data in Banten Province from 2020 to 2025. The research process was carried out through several stages, namely data collection, data preprocessing, social stability label determination, splitting training and testing data, Random Forest modeling, model evaluation, feature importance analysis, and result visualization using the Python programming language. The Random Forest algorithm was selected because of its ability to perform classification effectively and reduce overfitting in classification models. The evaluation results showed that the Random Forest model achieved an accuracy value of 100%, with precision, recall, and F1-score values of 1.00. In addition, the feature importance analysis indicated that the unemployment variable had the strongest influence on the classification results, followed by HDI and poverty variables, while the Gini Ratio had the lowest influence. The findings of this study indicate that the Random Forest method can be effectively applied to classify regional social stability in Banten Province based on social indicators and can support data-driven regional policy analysis.

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