Smart Tools for Smart Learning: IoT-Based Landslide Early Warning System with TILT Sensors and Apps

Universitas Muhammadiyah Purworejo, Purworejo, Indonesia

^* Corresponding author: siskadesy@umpwr.ac.id

Abstract

This study focuses on the design, application, and validation of an IoT-based Landslide Early Warning System (EWS) utilizing TILT sensors and the BLINK application. The goal is to develop an educational yet functional system to enhance students’ understanding of landslide mechanics and real-time monitoring for early warning. The system integrates TILT sensors to detect slope movement and slope angle changes. These sensors are connected to the BLINK application, which serves as an easy-to-use interface for data visualization and real-time alerting. The system is then tested in a simulated environment to validate its accuracy. Results show a detection accuracy of 95% for slope instability events, with alerts sent to the BLINK application within milliseconds. Additionally, user trials involving high school students revealed significant improvements in their understanding of landslide triggers and the role of early warning systems. These findings highlight the dual benefits of this system as both an educational tool and a functional early warning tool. By combining IoT technology with hands-on learning, this approach bridges theoretical knowledge and practical application, empowering students to understand and mitigate landslide risks.