Socio-Technological Integration Model For Landslide Early Detection In Supporting Community Resilience: A Literature Review - Kartiko Ardi Widodo; Aryuanto Soetedjo; Iftitah Ruwana (International Journal of Technology and Education Research, 2025)
Landslides constitute disasters with significant impacts on lives, infrastructure, and the environment, particularly in regions with complex topography and high rainfall. The Indonesian National Disaster Management Agency (BNPB) recorded 2,726 disaster incidents in 2025, with landslides representing one of the primary threats. Early detection has become crucial for reducing casualty risks and losses. This study aims to analyze socio-technological integration models in landslide early detection systems to strengthen community resilience. The methodology employed is a systematic literature review of 34 studies encompassing qualitative, quantitative, and mixed-method approaches from 2010-2025. Bibliometric analysis through VOSviewer using data from Publish or Perish revealed 62 articles with a total of 2,563 citations, an h-index of 19, and a g-index of 50, indicating high scientific relevance. Network, overlay, and density visualizations were utilized to identify patterns and interrelationships among topics. Results demonstrate that integrating detection technologies such as sensors, machine learning, remote sensing, and LIDAR technology with community empowerment through education, community task force formation, and evacuation simulations produces systems that are more effective and sustainable compared to technology-based approaches alone. Key findings include the utilization of simple, easily-operated technology, the importance of village preparedness teams as sustainability drivers, rapid and accessible communication systems, and synergy between local knowledge and scientific data. Studies in Indonesia, South Asia, and globally demonstrate the success of this hybrid approach in local contexts. The socio-technological integration model is proven to enhance community resilience through technical, social, and institutional dimensions. This research confirms that effective landslide early detection requires a holistic approach balancing technological and social aspects, adaptable to various geographical conditions.