Decision Tree-Based Classification System for Elderly Social Aid Beneficiaries in Jakarta: Case Study Implementation in RW 13, Malaka Jaya Sub-Distric - Sutisna Sutisna; Hermawan Susanto (Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia, 2025)
Social assistance distribution to elderly populations in urban areas, particularly Jakarta, frequently encounters challenges in accurately identifying eligible recipients. The present study develops a classification model for elderly social assistance recipients using the Decision Tree algorithm to enhance objectivity and precision in beneficiary selection. Field research was conducted in RW 13, Malaka Jaya Sub-District, East Jakarta, utilizing primary data gathered through systematic observation. Key variables including age, income level, residential ownership status, health conditions, and family dependents were incorporated into the classification framework. The CRISP-DM methodology structured the analytical process, spanning from initial data exploration through model validation. Model testing employed a 70:30 data partition strategy, achieving 95.84% classification accuracy. Findings demonstrate the model's capability in determining eligibility for Jakarta's elderly social assistance program. Implementation of the proposed classification system promises to strengthen transparency, improve targeting precision, and establish evidence-based decision-making in social welfare distribution.