Optimizing Aid Distribution through a Blockchain-Based Precision Donation System to Accelerate Disaster Management at Lazismu in Tasikmalaya Regency

Optimalisasi Distribusi Bantuan melalui Sistem Donasi Presisi Berbasis Blockchain untuk Percepatan Penanganan Bencana di Lazismu Kabupaten Tasikmalaya

Abstract

Aid distribution during the disaster emergency response phase often faces various obstacles, such as inaccurate recipient data collection, uneven distribution, and low transaction transparency. This condition also occurs in the Lazismu Tasikmalaya Regency aid distribution process, especially during floods and landslides. This community service program aims to optimize aid distribution through the development of a Precision Donation System based on Blockchain Technology. Blockchain technology was chosen because it is able to provide transparency, accountability, and an immutable audit trail, so that every funds flow and goods can be real time monitored. The implementation method includes mapping partner needs, designing system architecture, implementing simple smart contracts for donation management, training Lazismu managers, and testing the system using disaster simulation data. The developed system is able to record donation transactions, validate aid recipients, and monitor logistics distribution with a higher accuracy level  than manual processes. Evaluation results show that the blockchain use  increases recording efficiency by up to 35%, accelerates the aid verification process, and increases public trust in the transparency of aid distribution. This program has a direct impact in the form of increasing the digital capacity of zakat institutions in disaster management, while simultaneously supporting the achievement of SDGs 11 (Sustainable Cities and Communities) and 16 (Strong Institutions). This implementation also aligns with BRIN's research focus on digital transformation in disaster management. Going forward, this system has the potential to be further developed with the integration of geospatial mapping and machine learning for more precise predictions of aid needs.