Implementation of the C4.5 Decision Tree Algorithm for Determining Dominant Factors in Village Welfare Based on Social and Infrastructure Indicators
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Abstract
This study aims to identify the dominant factors influencing village welfare based on social and infrastructure indicators using the C4.5 Decision Tree algorithm. The research was conducted in Laut Tador Sub-district with data obtained from the official Prodeskel Kemendagri database for the year 2024. A quantitative approach was employed by applying data mining techniques, specifically classification through the C4.5 Decision Tree, implemented with Python in Google Colaboratory. The dataset consisted of social indicators, including population, occupation, health personnel, water sources, and education, as well as infrastructure indicators, such as health facilities, energy sources, transportation, clean water, and schools. The results show that education and energy availability emerged as the most dominant factors influencing the welfare level of villages. The classification model produced a decision tree that effectively illustrates the relationships among indicators and achieved a reliable level of accuracy through testing. This study highlights the potential of data-driven approaches in supporting evidence-based policymaking for rural development, particularly in determining priority areas for improving village welfare.
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