Strategi Humanitarian Logistics untuk Penanganan Bangunan Rusak Post-Disaster Recovery: Sebuah Sintesis Konsep dan Rekomendasi untuk Indonesia

Indonesia, strategically located on the Pacific Ring of Fire and at the confluence of tectonic plates, is one of the world's most disaster-prone countries, frequently experiencing earthquakes, tsunamis, volcanic eruptions, and floods. The frequency and intensity of these disasters highlight the urgent need for effective and efficient post-disaster recovery strategies. However, the implementation of post-disaster recovery in Indonesia still faces significant challenges, including complex inter-agency coordination, limited resources, and a lack of a holistic approach that integrates various aspects of recovery. Problems are also often related to inefficient humanitarian logistics management, exacerbated by damaged infrastructure, inaccurate or delayed data, and a lack of integrated and real-time information systems. To address this crucial need, this study examines humanitarian logistics as a transformative approach to post-disaster recovery. Using qualitative literature, the study analyzes post-disaster recovery strategies from various countries and compares them with practices and challenges in Indonesia, including technological innovations in damage monitoring and building extraction. The study's findings suggest that adopting comprehensive humanitarian logistics principles, supported by the integration of modern technology and adaptive governance, could significantly enhance Indonesia's capacity to achieve faster, more equitable, and more resilient post-disaster recovery. Specifically, there are opportunities for Indonesia to improve the efficiency and accuracy of post-disaster damage assessments through the adoption of more sophisticated algorithmic innovations and deep learning techniques, such as domain adaptation-based multi-scale feature alignment, which has the potential to equip disaster response capacity with more detailed and objective assessments through high levels of automation.