Mobile Application for Integrated Forest and Land Fire Reporting utilizing AI and Community Participation for Disaster Mitigation
Abstract
Forest and Land Fires in Indonesia represent a chronic disaster with multidimensional impacts, marked by economic losses reaching Rp. 72.8 trillion in 2022 and severe data fragmentation. Conventional reporting systems, dominated by manual mechanisms (85%), create a temporal crisis, causing response delays of 24 to 48 hours. This research aims to design and develop an integrated mobile application prototype that combines predictive Artificial Intelligence (AI) with community participation (crowdsourcing) to address this gap. The methodology used is Research and Development (R&D), beginning with an in-depth needs analysis of 150 respondents in Riau. A three-tier system architecture is implemented, consisting of a Mobile Layer (Flutter), a Firebase-based Backend as a Service (BaaS), and a Machine Learning Engine (TensorFlow) with a Random Forest (RF) model optimized for peatland characteristics. Initial results show an RF model accuracy of >= 80% on internal validation data and 90% user approval for the minimalist UI/UX design. This prototype is explicitly engineered to achieve a system response time of < 1 minute and a prediction accuracy of >= 85%, making it an innovative solution that enhances response speed, operational resilience, and disaster mitigation effectiveness in Forest and Land Fires-prone areas like Riau Province.
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Copyright (c) 2025 Edwar Ali, Khairani Djahara, Rian Pradewanta
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